🎯 Predykcja

In [1]:

import os
import pandas as pd
import matplotlib.pyplot as plt

import os import pandas as pd import matplotlib.pyplot as plt

In [2]:

# Folder where the CSV files are stored
input_folder = '../../data/scalone_dane'

# Initialize an empty list to store all data
all_data = []

# Dictionary to hold each city's DataFrame
city_dataframes = {}

# Loop through each CSV file in the folder
for filename in os.listdir(input_folder):
    if filename.endswith('_scalony.csv'):
        city = filename.replace('_scalony.csv', '')  # Extract city name
        file_path = os.path.join(input_folder, filename)
        
        # Read CSV and add a 'CITY' column
        df = pd.read_csv(file_path)
        df['CITY'] = city
        
        # Store individual city DataFrame in dictionary
        city_dataframes[city] = df.copy()
        
        # Add to the list for the combined DataFrame
        all_data.append(df)

# Combine all into a single DataFrame
combined_df = pd.concat(all_data, ignore_index=True)

print(f"Loaded data from {len(all_data)} cities. Total rows: {len(combined_df)}")

# Folder where the CSV files are stored input_folder = '../../data/scalone_dane' # Initialize an empty list to store all data all_data = [] # Dictionary to hold each city's DataFrame city_dataframes = {} # Loop through each CSV file in the folder for filename in os.listdir(input_folder): if filename.endswith('_scalony.csv'): city = filename.replace('_scalony.csv', '') # Extract city name file_path = os.path.join(input_folder, filename) # Read CSV and add a 'CITY' column df = pd.read_csv(file_path) df['CITY'] = city # Store individual city DataFrame in dictionary city_dataframes[city] = df.copy() # Add to the list for the combined DataFrame all_data.append(df) # Combine all into a single DataFrame combined_df = pd.concat(all_data, ignore_index=True) print(f"Loaded data from {len(all_data)} cities. Total rows: {len(combined_df)}")

Loaded data from 12 cities. Total rows: 218047

In [3]:

# Generate separate plots for each city
for city, df in city_dataframes.items():
    plt.figure(figsize=(12, 5))
    plt.plot(df.index, df['TEMP'], label=f'Temperatura - {city}')
    plt.title(f'Temperatura w czasie – {city}')
    plt.xlabel('Data')
    plt.ylabel('Temperatura')
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.show()

# Generate separate plots for each city for city, df in city_dataframes.items(): plt.figure(figsize=(12, 5)) plt.plot(df.index, df['TEMP'], label=f'Temperatura - {city}') plt.title(f'Temperatura w czasie – {city}') plt.xlabel('Data') plt.ylabel('Temperatura') plt.legend() plt.grid(True) plt.tight_layout() plt.show()

In [4]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score

# 1. Wczytanie danych
df = city_dataframes['Ateny'].copy()

# 2. Czyszczenie i przygotowanie
# Usunięcie kolumn typu "ATTRIBUTES", które są jakościowe i często puste
df = df[[col for col in df.columns if 'ATTRIBUTES' not in col]]

# Usunięcie kolumn niezmiennych lub zbędnych dla predykcji
df = df.drop(columns=['STATION', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'NAME', 'CITY'])

# Przekształcenie kolumny DATE do daty i dodanie cech czasowych
df['DATE'] = pd.to_datetime(df['DATE'], errors='coerce')
df['Year'] = df['DATE'].dt.year
df['Month'] = df['DATE'].dt.month
df['Day'] = df['DATE'].dt.day
df = df.drop(columns=['DATE'])

# Usunięcie wierszy z brakami danych
df = df.dropna()

# 3. Korelacja z celem (TEMP)
correlations = df.corr(numeric_only=True)['TEMP'].sort_values(ascending=False)

# Wyświetlenie korelacji
print("Korelacje z TEMP:")
print(correlations)

# Wykres korelacji
plt.figure(figsize=(10, 6))
sns.barplot(x=correlations.values, y=correlations.index)
plt.title("Korelacja cech z temperaturą (TEMP)")
plt.xlabel("Współczynnik korelacji")
plt.ylabel("Cechy")
plt.tight_layout()
plt.show()

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import r2_score # 1. Wczytanie danych df = city_dataframes['Ateny'].copy() # 2. Czyszczenie i przygotowanie # Usunięcie kolumn typu "ATTRIBUTES", które są jakościowe i często puste df = df[[col for col in df.columns if 'ATTRIBUTES' not in col]] # Usunięcie kolumn niezmiennych lub zbędnych dla predykcji df = df.drop(columns=['STATION', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'NAME', 'CITY']) # Przekształcenie kolumny DATE do daty i dodanie cech czasowych df['DATE'] = pd.to_datetime(df['DATE'], errors='coerce') df['Year'] = df['DATE'].dt.year df['Month'] = df['DATE'].dt.month df['Day'] = df['DATE'].dt.day df = df.drop(columns=['DATE']) # Usunięcie wierszy z brakami danych df = df.dropna() # 3. Korelacja z celem (TEMP) correlations = df.corr(numeric_only=True)['TEMP'].sort_values(ascending=False) # Wyświetlenie korelacji print("Korelacje z TEMP:") print(correlations) # Wykres korelacji plt.figure(figsize=(10, 6)) sns.barplot(x=correlations.values, y=correlations.index) plt.title("Korelacja cech z temperaturą (TEMP)") plt.xlabel("Współczynnik korelacji") plt.ylabel("Cechy") plt.tight_layout() plt.show()

Korelacje z TEMP:
TEMP      1.000000
MAX       0.977828
MIN       0.947702
DEWP      0.808331
Month     0.293717
MXSPD     0.145355
VISIB     0.144031
WDSP      0.103878
Year      0.044437
Day       0.009142
GUST     -0.076934
FRSHTT   -0.174427
PRCP     -0.323678
SLP            NaN
STP            NaN
SNDP           NaN
Name: TEMP, dtype: float64

In [5]:

from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools.tools import add_constant

# Zakładamy, że df już nie zawiera TEMP
features = df.drop(columns=['TEMP']).copy()

# Dodaj stałą (bias term) potrzebną do obliczenia VIF
features_with_const = add_constant(features)

# Obliczanie VIF
vif_data = pd.DataFrame()
vif_data["Feature"] = features_with_const.columns
vif_data["VIF"] = [variance_inflation_factor(features_with_const.values, i)
                   for i in range(features_with_const.shape[1])]

print("\n📊 Współczynniki VIF (Variance Inflation Factor):")
print(vif_data.sort_values(by="VIF", ascending=False))

from statsmodels.stats.outliers_influence import variance_inflation_factor from statsmodels.tools.tools import add_constant # Zakładamy, że df już nie zawiera TEMP features = df.drop(columns=['TEMP']).copy() # Dodaj stałą (bias term) potrzebną do obliczenia VIF features_with_const = add_constant(features) # Obliczanie VIF vif_data = pd.DataFrame() vif_data["Feature"] = features_with_const.columns vif_data["VIF"] = [variance_inflation_factor(features_with_const.values, i) for i in range(features_with_const.shape[1])] print("\n📊 Współczynniki VIF (Variance Inflation Factor):") print(vif_data.sort_values(by="VIF", ascending=False))

📊 Współczynniki VIF (Variance Inflation Factor):
   Feature        VIF
8      MIN  10.307664
7      MAX   8.578652
4     WDSP   8.184609
5    MXSPD   6.237028
0     DEWP   4.311201
6     GUST   1.815506
9     PRCP   1.637067
11  FRSHTT   1.269011
13   Month   1.189234
3    VISIB   1.039370
12    Year   1.019603
14     Day   1.001919
10    SNDP   0.000026
2      STP   0.000006
1      SLP   0.000000

C:\Users\mikos\AppData\Local\Programs\Python\Python312\Lib\site-packages\statsmodels\regression\linear_model.py:1782: RuntimeWarning: divide by zero encountered in scalar divide
  return 1 - self.ssr/self.centered_tss

In [6]:

selected_features = ['DEWP', 'Month', 'PRCP', 'VISIB']

selected_features = ['DEWP', 'Month', 'PRCP', 'VISIB']

In [7]:

import numpy as np
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_squared_error, mean_absolute_error
import warnings
import pandas as pd
from itertools import product
import seaborn as sns
from statsmodels.tsa.stattools import adfuller
from tqdm import tqdm

import numpy as np from statsmodels.tsa.statespace.sarimax import SARIMAX from sklearn.metrics import mean_squared_error, mean_absolute_error import warnings import pandas as pd from itertools import product import seaborn as sns from statsmodels.tsa.stattools import adfuller from tqdm import tqdm

In [8]:

warnings.filterwarnings("ignore")

warnings.filterwarnings("ignore")

In [9]:

results_summary = []


# Parametry
exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB']
forecast_horizon = 1
test_size_ratio = 0.10
validation_size_ratio = 0.15
last_n_years = 2

results_summary = [] # Parametry exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB'] forecast_horizon = 1 test_size_ratio = 0.10 validation_size_ratio = 0.15 last_n_years = 2

In [10]:

seasonal_order = (1, 1, 1, 365)
# plot_path = f"plots/{city}__sarimax_plot.png"
forecast_results = []
for city, df in city_dataframes.items():
    plot_path = f"../../plots/{city}__sarimax_plot.png"
    data = df.copy()

    # Filtrujemy tylko ostatnie 2 lata danych
    data.reset_index(inplace=True)
    data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce')
    data['Month'] = data['DATE'].dt.month
    data.set_index('DATE', inplace=True)
    
    last_date = data.index.max()
    cutoff_date = last_date - pd.DateOffset(years=last_n_years)
    data = data[data.index >= cutoff_date]

    data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce')
    data = data.dropna(subset=['TEMP'] + exog_vars)


    n_total = len(data)
    n_test = int(test_size_ratio * n_total)
    n_val = int(validation_size_ratio * (n_total - n_test))
    n_train = n_total - n_val - n_test

    train = data.iloc[:n_train]
    val = data.iloc[n_train:n_train + n_val]
    test = data.iloc[n_train + n_val:]


    combined_train = pd.concat([train, val])
    best_aicc = np.inf
    best_order = (0, 0, 0)
    # p = d = q = range(3)
    # candidate_orders = list(product(p, d, q))


    candidate_orders = list(product(range(3), repeat=3))  # or range(5)
    for order in tqdm(candidate_orders, desc=f"{city} model selection"):
        print(order)
        try:
            
            model = SARIMAX(combined_train['TEMP'], exog=combined_train[exog_vars], order=order, #seasonal_order=(1,1,1,365),
                            enforce_stationarity=False, enforce_invertibility=False)
            print('2')
            res = model.fit(disp=True)
            print('1')
            aicc = res.aic + (2 * len(res.params)**2 + 2 * len(res.params)) / (len(train) - len(res.params) - 1)
            residuals = res.resid
            # print('1')
            adf_result = adfuller(residuals.dropna())
            # print('1')
            p_value = adf_result[1]
            if p_value < 0.05 and aicc < best_aicc:
                best_aicc = aicc
                best_order = order
            print(f"Order: {order}, AICc: {aicc:.2f}, ADF p-value: {p_value:.4f}, Best_Order: {best_order}, Best_aicc: {best_aicc:.2f}")
        except:
            continue


    # Rolling forecast: iteracyjne prognozowanie 1 kroku do przodu
    history_y = combined_train['TEMP'].tolist()
    history_exog = combined_train[exog_vars].copy()
    predictions, lowers, uppers, actuals = [], [], [], []

    for t in tqdm(range(len(test)), desc=f"Analysed test_samples from: "):
        try:
            print('1')
            model = SARIMAX(history_y, exog=history_exog, order=best_order, #seasonal_order=(1,1,1,365),
                            enforce_stationarity=False, enforce_invertibility=False)
            model_fit = model.fit(disp=False)

            next_exog = test[exog_vars].iloc[[t]]
            pred = model_fit.get_forecast(steps=1, exog=next_exog)
            pred_mean = pred.predicted_mean.iloc[0]
            ci = pred.conf_int(alpha=0.05).iloc[0]
            print(pred_mean)
            predictions.append(pred_mean)
            lowers.append(ci[0])
            uppers.append(ci[1])
            actuals.append(test['TEMP'].iloc[t])
            if not np.isnan(pred_mean):
                history_y.append(pred_mean)
                history_exog = pd.concat([history_exog, next_exog])
            else:
                print(f"⚠️ Skipped step {t} due to NaN prediction.")
            continue
            # history_y.append(pred_mean)  
            # history_exog = pd.concat([history_exog, next_exog])
        except:
            predictions.append(np.nan)
            lowers.append(np.nan)
            uppers.append(np.nan)
            actuals.append(test['TEMP'].iloc[t])



    # Ewaluacja
    y_true = np.array(actuals)
    y_pred = np.array(predictions)

    print(np.isnan(y_true).any())
    print(np.isnan(y_pred).any())

    mae = mean_absolute_error(y_true, y_pred)
    rmse = np.sqrt(mean_squared_error(y_true, y_pred))

    forecast_results.append({
        'City': city,
        'Best Order': best_order,
        'Test MAE': mae,
        'Test RMSE': rmse,
        'AICc': best_aicc
    })

    
    # Wykres
    plt.figure(figsize=(15, 6))
    plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue')
    plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange')
    plt.plot(data.index[n_train+n_val:], actuals, label='Test Actual', color='green')
    plt.plot(data.index[n_train+n_val:], predictions, label='Forecast', color='red')
    plt.fill_between(data.index[n_train+n_val:], lowers, uppers, color='red', alpha=0.3, label='95% CI')
    plt.title(f"SARIMAX Forecast with 95% CI — Order {best_order}")
    plt.xlabel("Date")
    plt.ylabel("Temperature")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(plot_path)
    plt.show()

seasonal_order = (1, 1, 1, 365) # plot_path = f"plots/{city}__sarimax_plot.png" forecast_results = [] for city, df in city_dataframes.items(): plot_path = f"../../plots/{city}__sarimax_plot.png" data = df.copy() # Filtrujemy tylko ostatnie 2 lata danych data.reset_index(inplace=True) data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce') data['Month'] = data['DATE'].dt.month data.set_index('DATE', inplace=True) last_date = data.index.max() cutoff_date = last_date - pd.DateOffset(years=last_n_years) data = data[data.index >= cutoff_date] data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce') data = data.dropna(subset=['TEMP'] + exog_vars) n_total = len(data) n_test = int(test_size_ratio * n_total) n_val = int(validation_size_ratio * (n_total - n_test)) n_train = n_total - n_val - n_test train = data.iloc[:n_train] val = data.iloc[n_train:n_train + n_val] test = data.iloc[n_train + n_val:] combined_train = pd.concat([train, val]) best_aicc = np.inf best_order = (0, 0, 0) # p = d = q = range(3) # candidate_orders = list(product(p, d, q)) candidate_orders = list(product(range(3), repeat=3)) # or range(5) for order in tqdm(candidate_orders, desc=f"{city} model selection"): print(order) try: model = SARIMAX(combined_train['TEMP'], exog=combined_train[exog_vars], order=order, #seasonal_order=(1,1,1,365), enforce_stationarity=False, enforce_invertibility=False) print('2') res = model.fit(disp=True) print('1') aicc = res.aic + (2 * len(res.params)**2 + 2 * len(res.params)) / (len(train) - len(res.params) - 1) residuals = res.resid # print('1') adf_result = adfuller(residuals.dropna()) # print('1') p_value = adf_result[1] if p_value < 0.05 and aicc < best_aicc: best_aicc = aicc best_order = order print(f"Order: {order}, AICc: {aicc:.2f}, ADF p-value: {p_value:.4f}, Best_Order: {best_order}, Best_aicc: {best_aicc:.2f}") except: continue # Rolling forecast: iteracyjne prognozowanie 1 kroku do przodu history_y = combined_train['TEMP'].tolist() history_exog = combined_train[exog_vars].copy() predictions, lowers, uppers, actuals = [], [], [], [] for t in tqdm(range(len(test)), desc=f"Analysed test_samples from: "): try: print('1') model = SARIMAX(history_y, exog=history_exog, order=best_order, #seasonal_order=(1,1,1,365), enforce_stationarity=False, enforce_invertibility=False) model_fit = model.fit(disp=False) next_exog = test[exog_vars].iloc[[t]] pred = model_fit.get_forecast(steps=1, exog=next_exog) pred_mean = pred.predicted_mean.iloc[0] ci = pred.conf_int(alpha=0.05).iloc[0] print(pred_mean) predictions.append(pred_mean) lowers.append(ci[0]) uppers.append(ci[1]) actuals.append(test['TEMP'].iloc[t]) if not np.isnan(pred_mean): history_y.append(pred_mean) history_exog = pd.concat([history_exog, next_exog]) else: print(f"⚠️ Skipped step {t} due to NaN prediction.") continue # history_y.append(pred_mean) # history_exog = pd.concat([history_exog, next_exog]) except: predictions.append(np.nan) lowers.append(np.nan) uppers.append(np.nan) actuals.append(test['TEMP'].iloc[t]) # Ewaluacja y_true = np.array(actuals) y_pred = np.array(predictions) print(np.isnan(y_true).any()) print(np.isnan(y_pred).any()) mae = mean_absolute_error(y_true, y_pred) rmse = np.sqrt(mean_squared_error(y_true, y_pred)) forecast_results.append({ 'City': city, 'Best Order': best_order, 'Test MAE': mae, 'Test RMSE': rmse, 'AICc': best_aicc }) # Wykres plt.figure(figsize=(15, 6)) plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue') plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange') plt.plot(data.index[n_train+n_val:], actuals, label='Test Actual', color='green') plt.plot(data.index[n_train+n_val:], predictions, label='Forecast', color='red') plt.fill_between(data.index[n_train+n_val:], lowers, uppers, color='red', alpha=0.3, label='95% CI') plt.title(f"SARIMAX Forecast with 95% CI — Order {best_order}") plt.xlabel("Date") plt.ylabel("Temperature") plt.legend() plt.grid(True) plt.tight_layout() plt.savefig(plot_path) plt.show()

Ateny model selection:   0%|                                                                    | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
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Ateny model selection:   4%|██▏                                                         | 1/27 [00:00<00:07,  3.43it/s]

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Order: (0, 0, 0), AICc: 4396.07, ADF p-value: 0.0241, Best_Order: (0, 0, 0), Best_aicc: 4396.07
(0, 0, 1)
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Ateny model selection:   7%|████▍                                                       | 2/27 [00:00<00:08,  2.90it/s]

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Order: (0, 0, 1), AICc: 3953.59, ADF p-value: 0.0857, Best_Order: (0, 0, 0), Best_aicc: 4396.07
(0, 0, 2)
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Ateny model selection:  11%|██████▋                                                     | 3/27 [00:01<00:09,  2.57it/s]

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Order: (0, 0, 2), AICc: 3728.44, ADF p-value: 0.2754, Best_Order: (0, 0, 0), Best_aicc: 4396.07
(0, 1, 0)
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Ateny model selection:  15%|████████▉                                                   | 4/27 [00:01<00:08,  2.84it/s]

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Order: (0, 1, 0), AICc: 3105.77, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3105.77
(0, 1, 1)
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Ateny model selection:  19%|███████████                                                 | 5/27 [00:01<00:07,  3.04it/s]

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Order: (0, 1, 1), AICc: 3102.88, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3102.88
(0, 1, 2)
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Ateny model selection:  22%|█████████████▎                                              | 6/27 [00:02<00:07,  2.84it/s]

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Order: (0, 1, 2), AICc: 3086.43, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
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Ateny model selection:  26%|███████████████▌                                            | 7/27 [00:02<00:06,  3.01it/s]

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Order: (0, 2, 0), AICc: 3554.22, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
(0, 2, 1)
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Ateny model selection:  30%|█████████████████▊                                          | 8/27 [00:02<00:07,  2.63it/s]

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Order: (0, 2, 1), AICc: 3104.95, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
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Ateny model selection:  33%|████████████████████                                        | 9/27 [00:03<00:07,  2.30it/s]

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Order: (0, 2, 2), AICc: 3103.72, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
(1, 0, 0)
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Ateny model selection:  37%|█████████████████████▊                                     | 10/27 [00:03<00:07,  2.40it/s]

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Order: (1, 0, 0), AICc: 3112.79, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
(1, 0, 1)
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Ateny model selection:  41%|████████████████████████                                   | 11/27 [00:04<00:06,  2.29it/s]

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Order: (1, 0, 1), AICc: 3109.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
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Ateny model selection:  44%|██████████████████████████▏                                | 12/27 [00:04<00:07,  2.08it/s]

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Order: (1, 0, 2), AICc: 3092.68, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
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Ateny model selection:  48%|████████████████████████████▍                              | 13/27 [00:05<00:05,  2.45it/s]

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Order: (1, 1, 0), AICc: 3107.56, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3086.43
(1, 1, 1)
2

Ateny model selection:  52%|██████████████████████████████▌                            | 14/27 [00:05<00:05,  2.55it/s]

1
Order: (1, 1, 1), AICc: 3074.64, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3074.64
(1, 1, 2)
2

Ateny model selection:  56%|████████████████████████████████▊                          | 15/27 [00:05<00:04,  2.49it/s]

1
Order: (1, 1, 2), AICc: 3065.88, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(1, 2, 0)
2

Ateny model selection:  59%|██████████████████████████████████▉                        | 16/27 [00:06<00:03,  2.87it/s]

1
Order: (1, 2, 0), AICc: 3416.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(1, 2, 1)
2

Ateny model selection:  63%|█████████████████████████████████████▏                     | 17/27 [00:06<00:04,  2.43it/s]

1
Order: (1, 2, 1), AICc: 3106.91, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(1, 2, 2)
2

Ateny model selection:  67%|███████████████████████████████████████▎                   | 18/27 [00:07<00:04,  2.09it/s]

1
Order: (1, 2, 2), AICc: 3104.79, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 0, 0)
2

Ateny model selection:  70%|█████████████████████████████████████████▌                 | 19/27 [00:07<00:03,  2.10it/s]

1
Order: (2, 0, 0), AICc: 3109.98, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 0, 1)
2

Ateny model selection:  74%|███████████████████████████████████████████▋               | 20/27 [00:08<00:03,  1.75it/s]

1
Order: (2, 0, 1), AICc: 3114.78, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 0, 2)
2

Ateny model selection:  78%|█████████████████████████████████████████████▉             | 21/27 [00:09<00:03,  1.63it/s]

1
Order: (2, 0, 2), AICc: 3074.17, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 1, 0)
2

Ateny model selection:  81%|████████████████████████████████████████████████           | 22/27 [00:09<00:02,  1.94it/s]

1
Order: (2, 1, 0), AICc: 3095.69, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 1, 1)
2

Ateny model selection:  85%|██████████████████████████████████████████████████▎        | 23/27 [00:09<00:01,  2.04it/s]

1
Order: (2, 1, 1), AICc: 3070.17, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 1, 2)
2

Ateny model selection:  89%|████████████████████████████████████████████████████▍      | 24/27 [00:10<00:01,  1.90it/s]

1
Order: (2, 1, 2), AICc: 3067.51, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 2, 0)
2

Ateny model selection:  93%|██████████████████████████████████████████████████████▋    | 25/27 [00:10<00:00,  2.22it/s]

1
Order: (2, 2, 0), AICc: 3340.22, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 2, 1)
2

Ateny model selection:  96%|████████████████████████████████████████████████████████▊  | 26/27 [00:11<00:00,  2.02it/s]

1
Order: (2, 2, 1), AICc: 3100.73, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88
(2, 2, 2)
2

Ateny model selection: 100%|███████████████████████████████████████████████████████████| 27/27 [00:12<00:00,  2.21it/s]

1
Order: (2, 2, 2), AICc: 3101.37, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3065.88

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:30,  2.32it/s]

67.30751577723834
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:34,  2.06it/s]

67.58020612561286
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:01<00:34,  2.04it/s]

68.31755407971727
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:35,  1.96it/s]

67.71570252700727
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:02<00:37,  1.82it/s]

66.17883403048066
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:02<00:32,  2.06it/s]

68.48956912067754
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:03<00:33,  1.99it/s]

68.80234455582575
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:03<00:32,  2.03it/s]

68.38275482900781
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:04<00:32,  1.94it/s]

67.81336824804666
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:05<00:31,  1.98it/s]

68.25288479270066
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:05<00:29,  2.08it/s]

67.56452565233826
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:05<00:28,  2.15it/s]

67.30627584721718
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:06<00:26,  2.25it/s]

67.67133095384028
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:06<00:24,  2.36it/s]

65.78528705259637
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:07<00:23,  2.44it/s]

67.96213960281582
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:07<00:23,  2.47it/s]

67.27325770808953
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:07<00:22,  2.50it/s]

65.82157259668278
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:08<00:21,  2.55it/s]

66.34243611646295
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:08<00:21,  2.53it/s]

66.40037772512548
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:09<00:22,  2.38it/s]

66.83440847601885
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:09<00:22,  2.28it/s]

66.08218886529023
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:10<00:22,  2.24it/s]

64.63174805072623
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:10<00:22,  2.22it/s]

66.97916023130715
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:11<00:24,  1.99it/s]

65.04060288208066
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:11<00:22,  2.13it/s]

65.00529121228288
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:11<00:20,  2.27it/s]

65.70606836510586
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:12<00:20,  2.28it/s]

66.54517953294632
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:12<00:19,  2.37it/s]

64.86704240371732
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:13<00:18,  2.39it/s]

65.64823530677515
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:13<00:17,  2.42it/s]

62.08961238283027
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:13<00:17,  2.46it/s]

58.53074044059675
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:14<00:17,  2.29it/s]

61.77122689343072
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:14<00:17,  2.33it/s]

64.75093615990308
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:15<00:16,  2.38it/s]

65.70699137334591
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:15<00:15,  2.39it/s]

65.99137159058182
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:16<00:16,  2.27it/s]

66.48732654673779
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:16<00:15,  2.40it/s]

63.64802993558176
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:16<00:14,  2.44it/s]

58.53051394074206
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:17<00:14,  2.42it/s]

58.9645033430087
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:17<00:13,  2.37it/s]

63.67447423538126
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:18<00:13,  2.35it/s]

64.94752309384153
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:18<00:13,  2.33it/s]

63.96979628708432
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:18<00:12,  2.46it/s]

65.26176497183258
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:19<00:12,  2.25it/s]

67.40231219649549
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:20<00:13,  2.08it/s]

67.69178859070107
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:20<00:13,  1.96it/s]

64.71074593261602
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:21<00:15,  1.72it/s]

63.985032008282914
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:21<00:14,  1.75it/s]

63.519544848370714
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:22<00:14,  1.67it/s]

63.699805639191375
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:23<00:12,  1.82it/s]

61.7034416570571
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:23<00:11,  1.98it/s]

61.147674854925796
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:23<00:10,  2.06it/s]

61.81305471519303
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:24<00:09,  2.17it/s]

63.49743644566179
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:24<00:08,  2.16it/s]

63.584195487951185
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:25<00:08,  2.02it/s]

64.62593132939047
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:25<00:08,  1.92it/s]

67.14326687269809
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:26<00:08,  1.86it/s]

66.1242458767255
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:26<00:07,  2.04it/s]

63.114988492319
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:27<00:06,  2.17it/s]

61.7034598620619
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:27<00:05,  2.25it/s]

60.198793806645995
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:28<00:05,  2.37it/s]

61.24019609502166
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:28<00:04,  2.38it/s]

62.075689595452886
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:28<00:04,  2.46it/s]

62.94374699365771
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:29<00:03,  2.34it/s]

63.58416893219419
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:29<00:03,  2.30it/s]

62.195130834805695
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:30<00:03,  1.96it/s]

63.96029180606662
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:30<00:02,  2.18it/s]

63.67101684214818
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:31<00:02,  2.35it/s]

64.56797200836436
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:31<00:01,  2.44it/s]

63.98924072967211
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:31<00:01,  2.39it/s]

63.15006552029184
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:32<00:00,  2.47it/s]

63.3526093949729
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:32<00:00,  2.43it/s]

63.728780803865625
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:33<00:00,  2.20it/s]

63.69984444235352
False
False

Berlin model selection:   0%|                                                                   | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Berlin model selection:   4%|██▏                                                        | 1/27 [00:00<00:06,  4.17it/s]

1
Order: (0, 0, 0), AICc: 4056.35, ADF p-value: 0.0117, Best_Order: (0, 0, 0), Best_aicc: 4056.35
(0, 0, 1)
2

Berlin model selection:   7%|████▎                                                      | 2/27 [00:00<00:06,  3.60it/s]

1
Order: (0, 0, 1), AICc: 3825.78, ADF p-value: 0.0136, Best_Order: (0, 0, 1), Best_aicc: 3825.78
(0, 0, 2)
2

Berlin model selection:  11%|██████▌                                                    | 3/27 [00:00<00:08,  2.89it/s]

1
Order: (0, 0, 2), AICc: 3740.32, ADF p-value: 0.0000, Best_Order: (0, 0, 2), Best_aicc: 3740.32
(0, 1, 0)
2

Berlin model selection:  19%|██████████▉                                                | 5/27 [00:01<00:05,  4.00it/s]

1
Order: (0, 1, 0), AICc: 3460.40, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3460.40
(0, 1, 1)
2
1
Order: (0, 1, 1), AICc: 3435.67, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3435.67
(0, 1, 2)
2

Berlin model selection:  22%|█████████████                                              | 6/27 [00:01<00:05,  3.62it/s]

1
Order: (0, 1, 2), AICc: 3401.93, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(0, 2, 0)
2

Berlin model selection:  26%|███████████████▎                                           | 7/27 [00:01<00:05,  3.88it/s]

1
Order: (0, 2, 0), AICc: 3956.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(0, 2, 1)
2

Berlin model selection:  30%|█████████████████▍                                         | 8/27 [00:02<00:05,  3.59it/s]

1
Order: (0, 2, 1), AICc: 3453.62, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(0, 2, 2)
2

Berlin model selection:  33%|███████████████████▋                                       | 9/27 [00:02<00:05,  3.13it/s]

1
Order: (0, 2, 2), AICc: 3438.31, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(1, 0, 0)
2

Berlin model selection:  37%|█████████████████████▍                                    | 10/27 [00:03<00:05,  2.95it/s]

1
Order: (1, 0, 0), AICc: 3467.10, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(1, 0, 1)
2

Berlin model selection:  41%|███████████████████████▋                                  | 11/27 [00:03<00:06,  2.42it/s]

1
Order: (1, 0, 1), AICc: 3445.58, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(1, 0, 2)
2

Berlin model selection:  44%|█████████████████████████▊                                | 12/27 [00:04<00:06,  2.18it/s]

1
Order: (1, 0, 2), AICc: 3408.12, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3451.88, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3401.93

Berlin model selection:  48%|███████████████████████████▉                              | 13/27 [00:04<00:05,  2.67it/s]

(1, 1, 1)
2

Berlin model selection:  52%|██████████████████████████████                            | 14/27 [00:04<00:05,  2.54it/s]

1
Order: (1, 1, 1), AICc: 3386.02, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3386.02
(1, 1, 2)
2

Berlin model selection:  56%|████████████████████████████████▏                         | 15/27 [00:05<00:06,  1.93it/s]

1
Order: (1, 1, 2), AICc: 3385.17, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(1, 2, 0)
2
1

Berlin model selection:  59%|██████████████████████████████████▎                       | 16/27 [00:05<00:04,  2.36it/s]

Order: (1, 2, 0), AICc: 3809.70, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(1, 2, 1)
2

Berlin model selection:  63%|████████████████████████████████████▌                     | 17/27 [00:06<00:04,  2.28it/s]

1
Order: (1, 2, 1), AICc: 3450.29, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(1, 2, 2)
2

Berlin model selection:  67%|██████████████████████████████████████▋                   | 18/27 [00:06<00:04,  2.19it/s]

1
Order: (1, 2, 2), AICc: 3454.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(2, 0, 0)
2

Berlin model selection:  70%|████████████████████████████████████████▊                 | 19/27 [00:07<00:03,  2.17it/s]

1
Order: (2, 0, 0), AICc: 3452.01, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(2, 0, 1)
2

Berlin model selection:  74%|██████████████████████████████████████████▉               | 20/27 [00:07<00:03,  2.08it/s]

1
Order: (2, 0, 1), AICc: 3403.47, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(2, 0, 2)
2

Berlin model selection:  78%|█████████████████████████████████████████████             | 21/27 [00:08<00:03,  1.88it/s]

1
Order: (2, 0, 2), AICc: 3411.14, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(2, 1, 0)
2

Berlin model selection:  81%|███████████████████████████████████████████████▎          | 22/27 [00:08<00:02,  2.29it/s]

1
Order: (2, 1, 0), AICc: 3420.92, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(2, 1, 1)
2

Berlin model selection:  85%|█████████████████████████████████████████████████▍        | 23/27 [00:09<00:02,  1.96it/s]

1
Order: (2, 1, 1), AICc: 3388.06, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3385.17
(2, 1, 2)
2

Berlin model selection:  89%|███████████████████████████████████████████████████▌      | 24/27 [00:09<00:01,  1.83it/s]

1
Order: (2, 1, 2), AICc: 3382.95, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95
(2, 2, 0)
2

Berlin model selection:  93%|█████████████████████████████████████████████████████▋    | 25/27 [00:10<00:00,  2.14it/s]

1
Order: (2, 2, 0), AICc: 3686.34, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95
(2, 2, 1)
2

Berlin model selection:  96%|███████████████████████████████████████████████████████▊  | 26/27 [00:10<00:00,  1.97it/s]

1
Order: (2, 2, 1), AICc: 3425.25, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95
(2, 2, 2)
2

Berlin model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:11<00:00,  2.34it/s]

1
Order: (2, 2, 2), AICc: 3428.10, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3382.95

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:43,  1.66it/s]

65.62735425396713
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:01<00:44,  1.60it/s]

66.04596746761403
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:01<00:43,  1.62it/s]

65.02259031015114
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:02<00:43,  1.60it/s]

65.33075436543933
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:03<00:42,  1.61it/s]

64.47700727681377
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:03<00:41,  1.62it/s]

65.82138840866403
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:04<00:41,  1.61it/s]

66.46641576242206
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:05<00:41,  1.56it/s]

67.96672518526083
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:05<00:40,  1.57it/s]

69.88989595777457
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:06<00:41,  1.53it/s]

68.3472613486198
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:07<00:40,  1.53it/s]

67.7661932727435
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:07<00:39,  1.54it/s]

64.7563071452109
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:08<00:39,  1.51it/s]

62.94145806156641
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:08<00:38,  1.52it/s]

65.66149156443565
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:09<00:37,  1.55it/s]

68.77876169382836
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:10<00:36,  1.57it/s]

67.273077266864
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:10<00:34,  1.60it/s]

65.79678817674902
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:11<00:34,  1.58it/s]

66.60840385878116
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:12<00:33,  1.60it/s]

70.09252839135985
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:12<00:33,  1.57it/s]

68.6417865579439
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:13<00:33,  1.55it/s]

66.0293951618142
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:14<00:33,  1.54it/s]

66.12845971629608
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:14<00:33,  1.51it/s]

67.04397706568977
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:15<00:32,  1.52it/s]

68.88766979844186
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:16<00:31,  1.55it/s]

65.48812996777579
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:16<00:30,  1.53it/s]

59.38658488836716
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:17<00:29,  1.56it/s]

61.424966951148505
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:17<00:28,  1.56it/s]

59.049593046501954
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:18<00:28,  1.56it/s]

57.63779152933907
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:19<00:26,  1.60it/s]

56.29560924655715
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:19<00:26,  1.58it/s]

60.55365144137927
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:20<00:26,  1.57it/s]

61.337528607639186
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:21<00:25,  1.58it/s]

60.80728420064129
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:21<00:24,  1.60it/s]

62.42423941010057
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:22<00:24,  1.57it/s]

62.901868751111536
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:23<00:23,  1.56it/s]

64.35923859683317
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:23<00:23,  1.52it/s]

62.72782336724349
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:24<00:22,  1.52it/s]

62.43468893508983
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:24<00:22,  1.54it/s]

63.58430169042256
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:25<00:21,  1.54it/s]

63.99906412550196
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:26<00:20,  1.56it/s]

62.80938804851184
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:26<00:20,  1.54it/s]

61.44899481253
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:27<00:19,  1.54it/s]

62.12163191455666
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:28<00:18,  1.53it/s]

63.19348301085421
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:28<00:18,  1.48it/s]

60.07348982268695
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:29<00:18,  1.48it/s]

57.763549191315846
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:30<00:17,  1.48it/s]

56.96433315579284
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:31<00:17,  1.45it/s]

59.54990980205554
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:31<00:16,  1.49it/s]

63.75640113170583
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:32<00:15,  1.50it/s]

65.02923975434707
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:33<00:14,  1.49it/s]

63.97926027850823
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:33<00:13,  1.50it/s]

64.03154517202826
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:34<00:13,  1.48it/s]

62.63677859792348
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:35<00:12,  1.47it/s]

65.14138419155714
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:35<00:12,  1.47it/s]

64.74996202272379
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:36<00:11,  1.49it/s]

64.58331309753723
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:37<00:10,  1.50it/s]

65.20096144367982
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:37<00:10,  1.47it/s]

63.4112012029537
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:38<00:09,  1.49it/s]

62.65985004200465
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:39<00:08,  1.50it/s]

59.24749887862634
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:39<00:07,  1.50it/s]

60.37201590819814
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:40<00:07,  1.48it/s]

61.831829502808965
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:41<00:06,  1.47it/s]

61.0264464323552
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:41<00:06,  1.47it/s]

63.80868571080629
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:42<00:05,  1.48it/s]

61.42898736417973
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:43<00:04,  1.47it/s]

59.73733562547491
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:43<00:04,  1.49it/s]

56.39526596975725
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:44<00:03,  1.49it/s]

61.442864739674874
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:45<00:02,  1.47it/s]

59.17066250521117
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:45<00:02,  1.46it/s]

62.03589437080747
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:46<00:01,  1.46it/s]

58.25804156264113
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:47<00:00,  1.43it/s]

58.189705758911245
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:48<00:00,  1.52it/s]

56.115361560734065
False
False

Bruksela model selection:   0%|                                                                 | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Bruksela model selection:   4%|██                                                       | 1/27 [00:00<00:06,  4.13it/s]

1
Order: (0, 0, 0), AICc: 3881.25, ADF p-value: 0.1478, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 0, 1)
2

Bruksela model selection:   7%|████▏                                                    | 2/27 [00:00<00:05,  4.32it/s]

1
Order: (0, 0, 1), AICc: 3552.08, ADF p-value: 0.1694, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 0, 2)
2

Bruksela model selection:  11%|██████▎                                                  | 3/27 [00:00<00:06,  3.83it/s]

1
Order: (0, 0, 2), AICc: 3445.11, ADF p-value: 0.2198, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 1, 0)
2
1
Order: (0, 1, 0), AICc: 3283.37, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3283.37

Bruksela model selection:  19%|██████████▌                                              | 5/27 [00:01<00:04,  5.00it/s]

(0, 1, 1)
2
1
Order: (0, 1, 1), AICc: 3266.65, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3266.65
(0, 1, 2)
2

Bruksela model selection:  22%|████████████▋                                            | 6/27 [00:01<00:04,  4.28it/s]

1
Order: (0, 1, 2), AICc: 3204.15, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(0, 2, 0)
2

Bruksela model selection:  26%|██████████████▊                                          | 7/27 [00:01<00:04,  4.37it/s]

1
Order: (0, 2, 0), AICc: 3784.23, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(0, 2, 1)
2

Bruksela model selection:  30%|████████████████▉                                        | 8/27 [00:01<00:04,  3.89it/s]

1
Order: (0, 2, 1), AICc: 3282.20, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(0, 2, 2)
2

Bruksela model selection:  33%|███████████████████                                      | 9/27 [00:02<00:06,  2.81it/s]

1
Order: (0, 2, 2), AICc: 3266.10, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(1, 0, 0)
2

Bruksela model selection:  37%|████████████████████▋                                   | 10/27 [00:02<00:05,  2.87it/s]

1
Order: (1, 0, 0), AICc: 3286.86, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(1, 0, 1)
2

Bruksela model selection:  41%|██████████████████████▊                                 | 11/27 [00:03<00:05,  2.92it/s]

1
Order: (1, 0, 1), AICc: 3270.14, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(1, 0, 2)
2

Bruksela model selection:  48%|██████████████████████████▉                             | 13/27 [00:03<00:04,  3.01it/s]

1
Order: (1, 0, 2), AICc: 3210.17, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3278.71, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3204.15
(1, 1, 1)
2

Bruksela model selection:  52%|█████████████████████████████                           | 14/27 [00:04<00:04,  3.07it/s]

1
Order: (1, 1, 1), AICc: 3202.23, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3202.23
(1, 1, 2)
2

Bruksela model selection:  56%|███████████████████████████████                         | 15/27 [00:04<00:03,  3.16it/s]

1
Order: (1, 1, 2), AICc: 3191.82, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(1, 2, 0)
2

Bruksela model selection:  59%|█████████████████████████████████▏                      | 16/27 [00:04<00:03,  3.42it/s]

1
Order: (1, 2, 0), AICc: 3651.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(1, 2, 1)
2

Bruksela model selection:  63%|███████████████████████████████████▎                    | 17/27 [00:05<00:03,  3.07it/s]

1
Order: (1, 2, 1), AICc: 3278.25, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(1, 2, 2)
2

Bruksela model selection:  67%|█████████████████████████████████████▎                  | 18/27 [00:05<00:03,  2.66it/s]

1
Order: (1, 2, 2), AICc: 3281.66, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 0, 0)
2

Bruksela model selection:  70%|███████████████████████████████████████▍                | 19/27 [00:05<00:02,  2.69it/s]

1
Order: (2, 0, 0), AICc: 3277.52, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 0, 1)
2

Bruksela model selection:  74%|█████████████████████████████████████████▍              | 20/27 [00:06<00:03,  2.16it/s]

1
Order: (2, 0, 1), AICc: 3211.44, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 0, 2)
2

Bruksela model selection:  78%|███████████████████████████████████████████▌            | 21/27 [00:07<00:03,  1.85it/s]

1
Order: (2, 0, 2), AICc: 3198.62, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 1, 0)
2

Bruksela model selection:  81%|█████████████████████████████████████████████▋          | 22/27 [00:07<00:02,  2.23it/s]

1
Order: (2, 1, 0), AICc: 3240.33, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 1, 1)
2

Bruksela model selection:  85%|███████████████████████████████████████████████▋        | 23/27 [00:07<00:01,  2.62it/s]

1
Order: (2, 1, 1), AICc: 3196.82, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 1, 2)
2

Bruksela model selection:  89%|█████████████████████████████████████████████████▊      | 24/27 [00:08<00:01,  2.33it/s]

1
Order: (2, 1, 2), AICc: 3193.86, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 2, 0)
2

Bruksela model selection:  93%|███████████████████████████████████████████████████▊    | 25/27 [00:08<00:00,  2.65it/s]

1
Order: (2, 2, 0), AICc: 3549.39, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 2, 1)
2

Bruksela model selection:  96%|█████████████████████████████████████████████████████▉  | 26/27 [00:09<00:00,  2.38it/s]

1
Order: (2, 2, 1), AICc: 3244.95, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82
(2, 2, 2)
2

Bruksela model selection: 100%|████████████████████████████████████████████████████████| 27/27 [00:09<00:00,  2.71it/s]

1
Order: (2, 2, 2), AICc: 3206.50, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3191.82

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:20,  3.59it/s]

60.81001150769126
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:18,  3.89it/s]

57.021253422707716
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:00<00:17,  3.94it/s]

57.03606452556754
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:17,  4.03it/s]

55.01907603090048
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:01<00:17,  3.89it/s]

55.948979111590106
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:01<00:16,  4.03it/s]

54.658572212946716
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:01<00:16,  4.05it/s]

54.103500021108545
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:02<00:16,  4.00it/s]

55.95261558620846
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:02<00:15,  4.07it/s]

56.17397681510489
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:02<00:15,  4.10it/s]

55.68982270831185
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:02<00:15,  4.10it/s]

56.66548505232444
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:02<00:14,  4.10it/s]

55.59166201959909
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:03<00:14,  4.11it/s]

56.28768461615111
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:03<00:14,  4.05it/s]

53.5841019455057
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:03<00:14,  4.10it/s]

51.219879735610576
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:03<00:13,  4.14it/s]

53.3326919553966
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:04<00:13,  4.18it/s]

54.36838768775253
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:04<00:13,  4.20it/s]

54.29955535061973
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:04<00:13,  4.12it/s]

52.843502151506506
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:04<00:12,  4.16it/s]

52.65302903101675
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:05<00:12,  4.11it/s]

52.651271787138796
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:05<00:12,  4.09it/s]

51.99497412458004
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:05<00:12,  4.13it/s]

51.44014402251629
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:05<00:12,  4.06it/s]

50.41515536370429
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:06<00:11,  4.02it/s]

55.54610945878348
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:06<00:11,  4.06it/s]

54.728938421270556
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:06<00:11,  4.08it/s]

53.35420269007324
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:06<00:11,  4.05it/s]

51.8395168163064
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:07<00:10,  4.01it/s]

50.643930287904624
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:07<00:10,  4.01it/s]

53.23902494095577
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:07<00:10,  4.05it/s]

56.12275162081973
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:07<00:10,  4.04it/s]

54.1165298561657
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:08<00:09,  4.02it/s]

52.20990181817291
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:08<00:10,  3.82it/s]

47.24249095731429
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:08<00:09,  3.87it/s]

54.3728755445471
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:08<00:09,  3.83it/s]

49.98605375393234
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:09<00:09,  3.89it/s]

49.074190758587264
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:09<00:09,  3.84it/s]

49.82133894673302
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:09<00:08,  3.81it/s]

49.922531820785906
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:09<00:08,  3.83it/s]

43.911201247764026
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:10<00:08,  3.79it/s]

46.674255019387836
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:10<00:08,  3.72it/s]

41.28191729291823
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:10<00:07,  3.86it/s]

40.88921155203411
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:11<00:07,  3.94it/s]

40.555314537242054
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:11<00:07,  4.00it/s]

41.59568943454349
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:11<00:06,  4.08it/s]

44.64359163464049
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:11<00:06,  4.12it/s]

45.801298075229155
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:11<00:06,  4.04it/s]

47.40695262177471
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:12<00:05,  4.01it/s]

44.941753193899046
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:12<00:05,  3.93it/s]

50.81491486660384
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:12<00:05,  3.83it/s]

52.34091738459968
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:13<00:05,  3.87it/s]

52.59518974727124
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:13<00:05,  3.88it/s]

53.01581413411577
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:13<00:04,  3.98it/s]

54.09160202261134
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:13<00:04,  4.00it/s]

53.15323396300221
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:14<00:04,  3.98it/s]

49.5853057650359
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:14<00:04,  3.92it/s]

49.94251474914698
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:14<00:03,  3.92it/s]

52.138533397277186
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:14<00:03,  3.96it/s]

49.39146973011266
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:15<00:03,  3.89it/s]

48.60537643082395
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:15<00:03,  3.89it/s]

51.01229964929651
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:15<00:02,  3.93it/s]

51.163694519011855
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:15<00:02,  3.94it/s]

54.089626509317085
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:16<00:02,  3.95it/s]

52.59671426401515
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:16<00:02,  3.86it/s]

52.98380259141444
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:16<00:01,  3.87it/s]

54.84801215166554
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:16<00:01,  3.89it/s]

54.20493709787149
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:17<00:01,  3.95it/s]

51.42861130220196
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:17<00:01,  3.93it/s]

51.36420471046843
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:17<00:00,  3.94it/s]

53.30673323384386
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:17<00:00,  3.99it/s]

53.54117629443261
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:18<00:00,  3.92it/s]

53.20547839426329
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:18<00:00,  3.97it/s]

51.2638528773447
False
False

Budapeszt model selection:   0%|                                                                | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Budapeszt model selection:   4%|██                                                      | 1/27 [00:00<00:08,  2.94it/s]

1
Order: (0, 0, 0), AICc: 4433.95, ADF p-value: 0.0018, Best_Order: (0, 0, 0), Best_aicc: 4433.95
(0, 0, 1)
2

Budapeszt model selection:   7%|████▏                                                   | 2/27 [00:00<00:09,  2.69it/s]

1
Order: (0, 0, 1), AICc: 4117.49, ADF p-value: 0.0741, Best_Order: (0, 0, 0), Best_aicc: 4433.95
(0, 0, 2)
2

Budapeszt model selection:  15%|████████▎                                               | 4/27 [00:01<00:07,  3.02it/s]

1
Order: (0, 0, 2), AICc: 3948.06, ADF p-value: 0.1780, Best_Order: (0, 0, 0), Best_aicc: 4433.95
(0, 1, 0)
2
1
Order: (0, 1, 0), AICc: 3490.36, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3490.36
(0, 1, 1)
2

Budapeszt model selection:  19%|██████████▎                                             | 5/27 [00:01<00:05,  3.74it/s]

1
Order: (0, 1, 1), AICc: 3478.47, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3478.47
(0, 1, 2)
2

Budapeszt model selection:  22%|████████████▍                                           | 6/27 [00:01<00:05,  3.90it/s]

1
Order: (0, 1, 2), AICc: 3425.83, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(0, 2, 0)
2

Budapeszt model selection:  26%|██████████████▌                                         | 7/27 [00:02<00:04,  4.23it/s]

1
Order: (0, 2, 0), AICc: 3960.10, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(0, 2, 1)
2

Budapeszt model selection:  30%|████████████████▌                                       | 8/27 [00:02<00:05,  3.59it/s]

1
Order: (0, 2, 1), AICc: 3485.14, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(0, 2, 2)
2

Budapeszt model selection:  33%|██████████████████▋                                     | 9/27 [00:02<00:06,  2.80it/s]

1
Order: (0, 2, 2), AICc: 3476.87, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(1, 0, 0)
2

Budapeszt model selection:  37%|████████████████████▎                                  | 10/27 [00:03<00:05,  2.97it/s]

1
Order: (1, 0, 0), AICc: 3481.83, ADF p-value: 0.0016, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(1, 0, 1)
2

Budapeszt model selection:  41%|██████████████████████▍                                | 11/27 [00:03<00:06,  2.61it/s]

1
Order: (1, 0, 1), AICc: 3475.36, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(1, 0, 2)
2

Budapeszt model selection:  48%|██████████████████████████▍                            | 13/27 [00:04<00:05,  2.74it/s]

1
Order: (1, 0, 2), AICc: 3431.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3488.77, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(1, 1, 1)
2

Budapeszt model selection:  52%|████████████████████████████▌                          | 14/27 [00:04<00:04,  2.97it/s]

1
Order: (1, 1, 1), AICc: 3426.20, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3425.83
(1, 1, 2)
2

Budapeszt model selection:  56%|██████████████████████████████▌                        | 15/27 [00:05<00:03,  3.04it/s]

1
Order: (1, 1, 2), AICc: 3411.65, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(1, 2, 0)
2
1

Budapeszt model selection:  59%|████████████████████████████████▌                      | 16/27 [00:05<00:03,  3.49it/s]

Order: (1, 2, 0), AICc: 3839.78, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(1, 2, 1)
2

Budapeszt model selection:  63%|██████████████████████████████████▋                    | 17/27 [00:05<00:03,  3.33it/s]

1
Order: (1, 2, 1), AICc: 3484.68, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(1, 2, 2)
2

Budapeszt model selection:  67%|████████████████████████████████████▋                  | 18/27 [00:06<00:03,  2.61it/s]

1
Order: (1, 2, 2), AICc: 3483.99, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 0, 0)
2

Budapeszt model selection:  70%|██████████████████████████████████████▋                | 19/27 [00:06<00:03,  2.48it/s]

1
Order: (2, 0, 0), AICc: 3476.72, ADF p-value: 0.0008, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 0, 1)
2

Budapeszt model selection:  74%|████████████████████████████████████████▋              | 20/27 [00:07<00:03,  2.26it/s]

1
Order: (2, 0, 1), AICc: 3496.21, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 0, 2)
2

Budapeszt model selection:  78%|██████████████████████████████████████████▊            | 21/27 [00:07<00:02,  2.02it/s]

1
Order: (2, 0, 2), AICc: 3522.94, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 1, 0)
2
1

Budapeszt model selection:  81%|████████████████████████████████████████████▊          | 22/27 [00:07<00:02,  2.48it/s]

Order: (2, 1, 0), AICc: 3455.50, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 1, 1)
2

Budapeszt model selection:  85%|██████████████████████████████████████████████▊        | 23/27 [00:08<00:01,  2.73it/s]

1
Order: (2, 1, 1), AICc: 3419.94, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 1, 2)
2

Budapeszt model selection:  89%|████████████████████████████████████████████████▉      | 24/27 [00:08<00:01,  2.56it/s]

1
Order: (2, 1, 2), AICc: 3413.64, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 2, 0)
2

Budapeszt model selection:  93%|██████████████████████████████████████████████████▉    | 25/27 [00:08<00:00,  2.93it/s]

1
Order: (2, 2, 0), AICc: 3741.82, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 2, 1)
2

Budapeszt model selection:  96%|████████████████████████████████████████████████████▉  | 26/27 [00:09<00:00,  2.49it/s]

1
Order: (2, 2, 1), AICc: 3459.78, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65
(2, 2, 2)
2

Budapeszt model selection: 100%|███████████████████████████████████████████████████████| 27/27 [00:10<00:00,  2.65it/s]

1
Order: (2, 2, 2), AICc: 3417.81, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3411.65

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:21,  3.37it/s]

55.171419468172544
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:19,  3.60it/s]

57.383272212710935
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:00<00:20,  3.42it/s]

56.18133361044262
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:20,  3.33it/s]

54.93486522007264
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:01<00:20,  3.25it/s]

56.38577807648289
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:01<00:19,  3.36it/s]

55.94445624613602
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:02<00:19,  3.37it/s]

55.64958667500238
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:02<00:19,  3.35it/s]

55.96501014033744
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:02<00:19,  3.36it/s]

54.621752813112806
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:02<00:18,  3.42it/s]

54.871327463503576
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:03<00:18,  3.34it/s]

54.95081635825984
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:03<00:18,  3.26it/s]

54.22023272799812
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:03<00:18,  3.22it/s]

52.94257297783219
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:04<00:18,  3.18it/s]

53.21881825946278
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:04<00:18,  3.12it/s]

54.04744189070413
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:04<00:17,  3.17it/s]

51.515703078891555
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:05<00:17,  3.19it/s]

53.77117696113525
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:05<00:16,  3.24it/s]

52.226448856029805
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:05<00:16,  3.30it/s]

53.731907197977215
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:06<00:16,  3.27it/s]

50.84590298418686
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:06<00:15,  3.34it/s]

49.8256910959653
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:06<00:14,  3.42it/s]

51.364400519558956
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:06<00:14,  3.41it/s]

51.50182100143126
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:07<00:13,  3.52it/s]

48.32640481501648
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:07<00:13,  3.50it/s]

50.18076781317771
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:07<00:13,  3.54it/s]

53.63267322187731
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:08<00:12,  3.54it/s]

51.94496127191063
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:08<00:13,  3.42it/s]

50.89089628648329
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:08<00:12,  3.40it/s]

50.757657980426096
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:08<00:13,  3.30it/s]

47.41879632730064
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:09<00:12,  3.37it/s]

48.28683013056282
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:09<00:11,  3.42it/s]

51.167093237752354
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:09<00:11,  3.39it/s]

50.96982926153988
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:10<00:11,  3.38it/s]

49.15486224380918
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:10<00:11,  3.34it/s]

46.98492260759619
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:10<00:11,  3.35it/s]

48.36585514260303
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:11<00:10,  3.46it/s]

45.60395882359695
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:11<00:10,  3.46it/s]

44.538691147708676
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:11<00:09,  3.42it/s]

46.90599512644454
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:11<00:09,  3.51it/s]

49.410785007507535
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:12<00:09,  3.48it/s]

44.42027723035327
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:12<00:09,  3.39it/s]

46.550920742217514
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:12<00:08,  3.36it/s]

47.69331962295414
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:13<00:08,  3.38it/s]

48.34255288449433
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:13<00:08,  3.37it/s]

43.2541054149945
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:13<00:08,  3.34it/s]

43.47653422714841
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:13<00:07,  3.30it/s]

45.84392486682155
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:14<00:07,  3.19it/s]

47.61863037454897
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:14<00:07,  3.16it/s]

47.405018805080125
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:14<00:07,  3.18it/s]

47.214144929517055
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:15<00:06,  3.16it/s]

46.988207499774305
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:15<00:06,  3.12it/s]

47.32841724806599
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:15<00:06,  3.11it/s]

47.885496681951295
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:16<00:06,  3.13it/s]

49.192847588550016
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:16<00:05,  3.11it/s]

50.14741146051948
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:16<00:05,  3.15it/s]

50.15355679967736
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:17<00:05,  3.11it/s]

48.842708565270094
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:17<00:04,  3.13it/s]

46.79093514008625
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:17<00:04,  3.09it/s]

47.7773701633514
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:18<00:04,  3.11it/s]

47.38279620841827
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:18<00:03,  3.09it/s]

46.080705960546894
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:18<00:03,  3.09it/s]

47.422219711301
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:19<00:03,  3.07it/s]

48.64538851438898
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:19<00:02,  3.06it/s]

47.97874130416601
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:19<00:02,  3.04it/s]

47.21415568563646
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:20<00:02,  3.09it/s]

49.34904919289276
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:20<00:01,  3.13it/s]

52.07786738464221
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:20<00:01,  3.15it/s]

50.69692358668749
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:21<00:01,  3.17it/s]

48.72416114322055
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:21<00:00,  3.19it/s]

48.72418550619625
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:21<00:00,  3.23it/s]

48.76365325433997
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:21<00:00,  3.26it/s]

49.0398584623111
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:22<00:00,  3.27it/s]

50.065724442941736
False
False

Lisbona model selection:   0%|                                                                  | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Lisbona model selection:   7%|████▎                                                     | 2/27 [00:00<00:05,  4.78it/s]

1
Order: (0, 0, 0), AICc: 4000.18, ADF p-value: 0.0000, Best_Order: (0, 0, 0), Best_aicc: 4000.18
(0, 0, 1)
2
1
Order: (0, 0, 1), AICc: 3687.00, ADF p-value: 0.0001, Best_Order: (0, 0, 1), Best_aicc: 3687.00
(0, 0, 2)
2

Lisbona model selection:  15%|████████▌                                                 | 4/27 [00:01<00:06,  3.82it/s]

1
Order: (0, 0, 2), AICc: 3541.99, ADF p-value: 0.0263, Best_Order: (0, 0, 2), Best_aicc: 3541.99
(0, 1, 0)
2
1
Order: (0, 1, 0), AICc: 3093.81, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3093.81
(0, 1, 1)
2

Lisbona model selection:  19%|██████████▋                                               | 5/27 [00:01<00:04,  4.58it/s]

1
Order: (0, 1, 1), AICc: 3090.77, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3090.77
(0, 1, 2)
2
1

Lisbona model selection:  22%|████████████▉                                             | 6/27 [00:01<00:04,  4.81it/s]

Order: (0, 1, 2), AICc: 3029.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(0, 2, 0)
2
1
Order: (0, 2, 0), AICc: 3528.96, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69

Lisbona model selection:  26%|███████████████                                           | 7/27 [00:01<00:03,  5.04it/s]

(0, 2, 1)
2

Lisbona model selection:  30%|█████████████████▏                                        | 8/27 [00:01<00:04,  3.91it/s]

1
Order: (0, 2, 1), AICc: 3093.24, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(0, 2, 2)
2

Lisbona model selection:  33%|███████████████████▎                                      | 9/27 [00:02<00:05,  3.10it/s]

1
Order: (0, 2, 2), AICc: 3086.83, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(1, 0, 0)
2

Lisbona model selection:  37%|█████████████████████                                    | 10/27 [00:02<00:05,  3.06it/s]

1
Order: (1, 0, 0), AICc: 3078.68, ADF p-value: 0.0112, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(1, 0, 1)
2

Lisbona model selection:  41%|███████████████████████▏                                 | 11/27 [00:03<00:06,  2.59it/s]

1
Order: (1, 0, 1), AICc: 3090.07, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(1, 0, 2)
2

Lisbona model selection:  48%|███████████████████████████▍                             | 13/27 [00:04<00:05,  2.73it/s]

1
Order: (1, 0, 2), AICc: 3143.61, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3095.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(1, 1, 1)
2

Lisbona model selection:  52%|█████████████████████████████▌                           | 14/27 [00:04<00:04,  3.01it/s]

1
Order: (1, 1, 1), AICc: 3038.97, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3029.69
(1, 1, 2)
2

Lisbona model selection:  59%|█████████████████████████████████▊                       | 16/27 [00:04<00:02,  3.92it/s]

1
Order: (1, 1, 2), AICc: 3016.25, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(1, 2, 0)
2
1
Order: (1, 2, 0), AICc: 3429.09, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(1, 2, 1)
2

Lisbona model selection:  63%|███████████████████████████████████▉                     | 17/27 [00:05<00:02,  3.34it/s]

1
Order: (1, 2, 1), AICc: 3095.28, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(1, 2, 2)
2

Lisbona model selection:  67%|██████████████████████████████████████                   | 18/27 [00:05<00:03,  2.75it/s]

1
Order: (1, 2, 2), AICc: 3082.57, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 0, 0)
2

Lisbona model selection:  70%|████████████████████████████████████████                 | 19/27 [00:06<00:03,  2.47it/s]

1
Order: (2, 0, 0), AICc: 3076.60, ADF p-value: 0.0136, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 0, 1)
2

Lisbona model selection:  74%|██████████████████████████████████████████▏              | 20/27 [00:06<00:03,  2.01it/s]

1
Order: (2, 0, 1), AICc: 3195.75, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 0, 2)
2

Lisbona model selection:  78%|████████████████████████████████████████████▎            | 21/27 [00:07<00:03,  1.80it/s]

1
Order: (2, 0, 2), AICc: 3225.54, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 1, 0)
2
1
Order: (2, 1, 0), AICc: 3057.07, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25

Lisbona model selection:  81%|██████████████████████████████████████████████▍          | 22/27 [00:07<00:02,  2.25it/s]

(2, 1, 1)
2

Lisbona model selection:  85%|████████████████████████████████████████████████▌        | 23/27 [00:08<00:01,  2.52it/s]

1
Order: (2, 1, 1), AICc: 3019.96, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 1, 2)
2

Lisbona model selection:  89%|██████████████████████████████████████████████████▋      | 24/27 [00:08<00:01,  2.52it/s]

1
Order: (2, 1, 2), AICc: 3017.68, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 2, 0)
2
1

Lisbona model selection:  93%|████████████████████████████████████████████████████▊    | 25/27 [00:08<00:00,  2.95it/s]

Order: (2, 2, 0), AICc: 3342.07, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 2, 1)
2

Lisbona model selection:  96%|██████████████████████████████████████████████████████▉  | 26/27 [00:08<00:00,  2.98it/s]

1
Order: (2, 2, 1), AICc: 3061.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25
(2, 2, 2)
2

Lisbona model selection: 100%|█████████████████████████████████████████████████████████| 27/27 [00:09<00:00,  2.80it/s]

1
Order: (2, 2, 2), AICc: 3021.59, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3016.25

Analysed test_samples from:   0%|                                                               | 0/70 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/70 [00:00<00:17,  3.98it/s]

55.638533504210656
1

Analysed test_samples from:   3%|█▌                                                     | 2/70 [00:00<00:17,  3.93it/s]

54.34913602586294
1

Analysed test_samples from:   4%|██▎                                                    | 3/70 [00:00<00:17,  3.91it/s]

55.581947377416775
1

Analysed test_samples from:   6%|███▏                                                   | 4/70 [00:01<00:17,  3.78it/s]

54.2796588053433
1

Analysed test_samples from:   7%|███▉                                                   | 5/70 [00:01<00:17,  3.69it/s]

54.83680029491033
1

Analysed test_samples from:   9%|████▋                                                  | 6/70 [00:01<00:17,  3.62it/s]

56.554787309373154
1

Analysed test_samples from:  10%|█████▌                                                 | 7/70 [00:01<00:17,  3.69it/s]

57.00424405027884
1

Analysed test_samples from:  11%|██████▎                                                | 8/70 [00:02<00:16,  3.80it/s]

57.474946931149795
1

Analysed test_samples from:  13%|███████                                                | 9/70 [00:02<00:15,  3.82it/s]

56.780300394964776
1

Analysed test_samples from:  14%|███████▋                                              | 10/70 [00:02<00:15,  3.80it/s]

57.13698042825364
1

Analysed test_samples from:  16%|████████▍                                             | 11/70 [00:02<00:15,  3.89it/s]

56.143105598330386
1

Analysed test_samples from:  17%|█████████▎                                            | 12/70 [00:03<00:14,  3.96it/s]

56.177022648277834
1

Analysed test_samples from:  19%|██████████                                            | 13/70 [00:03<00:14,  4.02it/s]

56.76094274411291
1

Analysed test_samples from:  20%|██████████▊                                           | 14/70 [00:03<00:13,  4.01it/s]

56.930625070022714
1

Analysed test_samples from:  21%|███████████▌                                          | 15/70 [00:03<00:13,  3.97it/s]

56.23478676507199
1

Analysed test_samples from:  23%|████████████▎                                         | 16/70 [00:04<00:13,  3.98it/s]

55.47108597816368
1

Analysed test_samples from:  24%|█████████████                                         | 17/70 [00:04<00:12,  4.08it/s]

56.31961238613876
1

Analysed test_samples from:  26%|█████████████▉                                        | 18/70 [00:04<00:12,  4.07it/s]

56.83885001295771
1

Analysed test_samples from:  27%|██████████████▋                                       | 19/70 [00:04<00:12,  4.07it/s]

57.30344138029423
1

Analysed test_samples from:  29%|███████████████▍                                      | 20/70 [00:05<00:12,  3.96it/s]

57.324873503656335
1

Analysed test_samples from:  30%|████████████████▏                                     | 21/70 [00:05<00:12,  3.87it/s]

57.466400663936895
1

Analysed test_samples from:  31%|████████████████▉                                     | 22/70 [00:05<00:12,  3.88it/s]

56.805448534775266
1

Analysed test_samples from:  33%|█████████████████▋                                    | 23/70 [00:05<00:12,  3.83it/s]

57.11389813321358
1

Analysed test_samples from:  34%|██████████████████▌                                   | 24/70 [00:06<00:12,  3.78it/s]

56.79512934365613
1

Analysed test_samples from:  36%|███████████████████▎                                  | 25/70 [00:06<00:12,  3.67it/s]

56.93088445148352
1

Analysed test_samples from:  37%|████████████████████                                  | 26/70 [00:06<00:11,  3.67it/s]

57.08361411766375
1

Analysed test_samples from:  39%|████████████████████▊                                 | 27/70 [00:07<00:11,  3.62it/s]

57.42941204706231
1

Analysed test_samples from:  40%|█████████████████████▌                                | 28/70 [00:07<00:11,  3.69it/s]

56.540571636158276
1

Analysed test_samples from:  41%|██████████████████████▎                               | 29/70 [00:07<00:10,  3.79it/s]

56.681377151938456
1

Analysed test_samples from:  43%|███████████████████████▏                              | 30/70 [00:07<00:10,  3.76it/s]

55.634026456029716
1

Analysed test_samples from:  44%|███████████████████████▉                              | 31/70 [00:08<00:10,  3.79it/s]

54.39265484712358
1

Analysed test_samples from:  46%|████████████████████████▋                             | 32/70 [00:08<00:10,  3.73it/s]

53.696869498821115
1

Analysed test_samples from:  47%|█████████████████████████▍                            | 33/70 [00:08<00:09,  3.72it/s]

53.78178168325519
1

Analysed test_samples from:  49%|██████████████████████████▏                           | 34/70 [00:08<00:09,  3.70it/s]

55.49835638682394
1

Analysed test_samples from:  50%|███████████████████████████                           | 35/70 [00:09<00:09,  3.67it/s]

56.08978612097363
1

Analysed test_samples from:  51%|███████████████████████████▊                          | 36/70 [00:09<00:09,  3.68it/s]

56.46314157245405
1

Analysed test_samples from:  53%|████████████████████████████▌                         | 37/70 [00:09<00:09,  3.46it/s]

57.17594634449375
1

Analysed test_samples from:  54%|█████████████████████████████▎                        | 38/70 [00:10<00:09,  3.42it/s]

58.08038471927539
1

Analysed test_samples from:  56%|██████████████████████████████                        | 39/70 [00:10<00:09,  3.37it/s]

57.54924819642701
1

Analysed test_samples from:  57%|██████████████████████████████▊                       | 40/70 [00:10<00:08,  3.36it/s]

57.49833473668315
1

Analysed test_samples from:  59%|███████████████████████████████▋                      | 41/70 [00:10<00:08,  3.41it/s]

57.1653254813356
1

Analysed test_samples from:  60%|████████████████████████████████▍                     | 42/70 [00:11<00:08,  3.50it/s]

57.141995033961166
1

Analysed test_samples from:  61%|█████████████████████████████████▏                    | 43/70 [00:11<00:07,  3.51it/s]

57.379583358215875
1

Analysed test_samples from:  63%|█████████████████████████████████▉                    | 44/70 [00:11<00:07,  3.53it/s]

57.41352950418796
1

Analysed test_samples from:  64%|██████████████████████████████████▋                   | 45/70 [00:12<00:07,  3.51it/s]

57.24382461324576
1

Analysed test_samples from:  66%|███████████████████████████████████▍                  | 46/70 [00:12<00:06,  3.47it/s]

57.09108370784904
1

Analysed test_samples from:  67%|████████████████████████████████████▎                 | 47/70 [00:12<00:06,  3.43it/s]

56.93834461757357
1

Analysed test_samples from:  69%|█████████████████████████████████████                 | 48/70 [00:12<00:06,  3.43it/s]

55.648589145313146
1

Analysed test_samples from:  70%|█████████████████████████████████████▊                | 49/70 [00:13<00:06,  3.27it/s]

55.66555281402784
1

Analysed test_samples from:  71%|██████████████████████████████████████▌               | 50/70 [00:13<00:06,  3.27it/s]

56.734692586724194
1

Analysed test_samples from:  73%|███████████████████████████████████████▎              | 51/70 [00:13<00:05,  3.28it/s]

56.84949887695948
1

Analysed test_samples from:  74%|████████████████████████████████████████              | 52/70 [00:14<00:05,  3.30it/s]

56.60551274991194
1

Analysed test_samples from:  76%|████████████████████████████████████████▉             | 53/70 [00:14<00:05,  3.31it/s]

57.05712610010603
1

Analysed test_samples from:  77%|█████████████████████████████████████████▋            | 54/70 [00:14<00:04,  3.31it/s]

57.20985894896014
1

Analysed test_samples from:  79%|██████████████████████████████████████████▍           | 55/70 [00:15<00:04,  3.31it/s]

57.82835331767006
1

Analysed test_samples from:  80%|███████████████████████████████████████████▏          | 56/70 [00:15<00:04,  3.20it/s]

57.294699655871824
1

Analysed test_samples from:  81%|███████████████████████████████████████████▉          | 57/70 [00:15<00:04,  3.11it/s]

57.14196677778301
1

Analysed test_samples from:  83%|████████████████████████████████████████████▋         | 58/70 [00:16<00:03,  3.09it/s]

56.95529618371146
1

Analysed test_samples from:  84%|█████████████████████████████████████████████▌        | 59/70 [00:16<00:03,  3.11it/s]

56.93832742628798
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▎       | 60/70 [00:16<00:03,  3.09it/s]

57.115466894582184
1

Analysed test_samples from:  87%|███████████████████████████████████████████████       | 61/70 [00:17<00:02,  3.10it/s]

57.35304371577831
1

Analysed test_samples from:  89%|███████████████████████████████████████████████▊      | 62/70 [00:17<00:02,  3.00it/s]

57.12052445897405
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▌     | 63/70 [00:17<00:02,  3.02it/s]

56.91186325923353
1

Analysed test_samples from:  91%|█████████████████████████████████████████████████▎    | 64/70 [00:18<00:01,  3.02it/s]

57.09852498931692
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▏   | 65/70 [00:18<00:01,  3.06it/s]

56.79309402059438
1

Analysed test_samples from:  94%|██████████████████████████████████████████████████▉   | 66/70 [00:18<00:01,  3.18it/s]

56.7761657519561
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▋  | 67/70 [00:19<00:00,  3.22it/s]

56.301025572563645
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▍ | 68/70 [00:19<00:00,  3.19it/s]

56.89494198862489
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▏| 69/70 [00:19<00:00,  3.25it/s]

57.30224965775684
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 70/70 [00:19<00:00,  3.51it/s]

56.419846653620056
False
False

Londyn model selection:   0%|                                                                   | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Londyn model selection:   4%|██▏                                                        | 1/27 [00:00<00:08,  3.12it/s]

1
Order: (0, 0, 0), AICc: 3531.24, ADF p-value: 0.0000, Best_Order: (0, 0, 0), Best_aicc: 3531.24
(0, 0, 1)
2

Londyn model selection:   7%|████▎                                                      | 2/27 [00:00<00:07,  3.31it/s]

1
Order: (0, 0, 1), AICc: 3247.42, ADF p-value: 0.0000, Best_Order: (0, 0, 1), Best_aicc: 3247.42
(0, 0, 2)
2

Londyn model selection:  11%|██████▌                                                    | 3/27 [00:00<00:07,  3.00it/s]

1
Order: (0, 0, 2), AICc: 3147.15, ADF p-value: 0.0000, Best_Order: (0, 0, 2), Best_aicc: 3147.15
(0, 1, 0)
2
1

Londyn model selection:  15%|████████▋                                                  | 4/27 [00:01<00:06,  3.55it/s]

Order: (0, 1, 0), AICc: 2978.23, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 2978.23
(0, 1, 1)
2
1
Order: (0, 1, 1), AICc: 2970.09, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 2970.09

Londyn model selection:  22%|█████████████                                              | 6/27 [00:01<00:04,  4.32it/s]

(0, 1, 2)
2
1
Order: (0, 1, 2), AICc: 2913.23, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23

Londyn model selection:  26%|███████████████▎                                           | 7/27 [00:01<00:04,  4.61it/s]

(0, 2, 0)
2
1
Order: (0, 2, 0), AICc: 3456.92, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(0, 2, 1)
2

Londyn model selection:  30%|█████████████████▍                                         | 8/27 [00:02<00:05,  3.65it/s]

1
Order: (0, 2, 1), AICc: 2977.13, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(0, 2, 2)
2

Londyn model selection:  33%|███████████████████▋                                       | 9/27 [00:02<00:06,  2.87it/s]

1
Order: (0, 2, 2), AICc: 2970.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(1, 0, 0)
2

Londyn model selection:  37%|█████████████████████▍                                    | 10/27 [00:03<00:05,  2.90it/s]

1
Order: (1, 0, 0), AICc: 2981.98, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(1, 0, 1)
2

Londyn model selection:  41%|███████████████████████▋                                  | 11/27 [00:03<00:05,  2.85it/s]

1
Order: (1, 0, 1), AICc: 2975.01, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(1, 0, 2)
2

Londyn model selection:  44%|█████████████████████████▊                                | 12/27 [00:03<00:06,  2.37it/s]

1
Order: (1, 0, 2), AICc: 2918.61, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(1, 1, 0)
2

Londyn model selection:  48%|███████████████████████████▉                              | 13/27 [00:04<00:04,  2.80it/s]

1
Order: (1, 1, 0), AICc: 2977.24, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 2913.23
(1, 1, 1)
2

Londyn model selection:  52%|██████████████████████████████                            | 14/27 [00:04<00:04,  2.74it/s]

1
Order: (1, 1, 1), AICc: 2904.19, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 2904.19
(1, 1, 2)
2

Londyn model selection:  56%|████████████████████████████████▏                         | 15/27 [00:04<00:04,  2.72it/s]

1
Order: (1, 1, 2), AICc: 2891.63, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(1, 2, 0)
2

Londyn model selection:  59%|██████████████████████████████████▎                       | 16/27 [00:05<00:03,  2.88it/s]

1
Order: (1, 2, 0), AICc: 3325.44, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(1, 2, 1)
2

Londyn model selection:  63%|████████████████████████████████████▌                     | 17/27 [00:05<00:03,  2.70it/s]

1
Order: (1, 2, 1), AICc: 2977.37, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(1, 2, 2)
2

Londyn model selection:  67%|██████████████████████████████████████▋                   | 18/27 [00:06<00:03,  2.49it/s]

1
Order: (1, 2, 2), AICc: 2977.02, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 0, 0)
2

Londyn model selection:  70%|████████████████████████████████████████▊                 | 19/27 [00:06<00:03,  2.45it/s]

1
Order: (2, 0, 0), AICc: 2976.98, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 0, 1)
2

Londyn model selection:  74%|██████████████████████████████████████████▉               | 20/27 [00:07<00:03,  2.14it/s]

1
Order: (2, 0, 1), AICc: 3001.97, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 0, 2)
2

Londyn model selection:  78%|█████████████████████████████████████████████             | 21/27 [00:07<00:03,  1.91it/s]

1
Order: (2, 0, 2), AICc: 2996.36, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 1, 0)
2
1

Londyn model selection:  81%|███████████████████████████████████████████████▎          | 22/27 [00:08<00:02,  2.33it/s]

Order: (2, 1, 0), AICc: 2947.42, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 1, 1)
2

Londyn model selection:  85%|█████████████████████████████████████████████████▍        | 23/27 [00:08<00:01,  2.51it/s]

1
Order: (2, 1, 1), AICc: 2895.85, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 1, 2)
2

Londyn model selection:  89%|███████████████████████████████████████████████████▌      | 24/27 [00:08<00:01,  2.62it/s]

1
Order: (2, 1, 2), AICc: 2893.32, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 2, 0)
2

Londyn model selection:  93%|█████████████████████████████████████████████████████▋    | 25/27 [00:08<00:00,  2.83it/s]

1
Order: (2, 2, 0), AICc: 3240.10, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 2, 1)
2

Londyn model selection:  96%|███████████████████████████████████████████████████████▊  | 26/27 [00:09<00:00,  2.55it/s]

1
Order: (2, 2, 1), AICc: 2952.40, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63
(2, 2, 2)
2

Londyn model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:10<00:00,  2.64it/s]

1
Order: (2, 2, 2), AICc: 2901.26, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 2891.63

Analysed test_samples from:   0%|                                                               | 0/72 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/72 [00:00<00:23,  3.07it/s]

53.700043525275596
1

Analysed test_samples from:   3%|█▌                                                     | 2/72 [00:00<00:24,  2.89it/s]

52.128507687567506
1

Analysed test_samples from:   4%|██▎                                                    | 3/72 [00:01<00:24,  2.81it/s]

53.53918838468976
1

Analysed test_samples from:   6%|███                                                    | 4/72 [00:01<00:21,  3.20it/s]

52.35377654490019
1

Analysed test_samples from:   7%|███▊                                                   | 5/72 [00:01<00:19,  3.48it/s]

51.746621572936064
1

Analysed test_samples from:   8%|████▌                                                  | 6/72 [00:01<00:21,  3.02it/s]

53.487163908133965
1

Analysed test_samples from:  10%|█████▎                                                 | 7/72 [00:02<00:22,  2.90it/s]

53.03800287357925
1

Analysed test_samples from:  11%|██████                                                 | 8/72 [00:02<00:19,  3.22it/s]

54.00379146973812
1

Analysed test_samples from:  12%|██████▉                                                | 9/72 [00:02<00:18,  3.36it/s]

52.31339117579628
1

Analysed test_samples from:  14%|███████▌                                              | 10/72 [00:03<00:19,  3.24it/s]

52.310426618758115
1

Analysed test_samples from:  15%|████████▎                                             | 11/72 [00:03<00:18,  3.23it/s]

53.68333454251308
1

Analysed test_samples from:  17%|█████████                                             | 12/72 [00:03<00:19,  3.12it/s]

52.7681885200096
1

Analysed test_samples from:  18%|█████████▊                                            | 13/72 [00:04<00:18,  3.22it/s]

50.46784121817875
1

Analysed test_samples from:  19%|██████████▌                                           | 14/72 [00:04<00:17,  3.28it/s]

50.491993637327695
1

Analysed test_samples from:  21%|███████████▎                                          | 15/72 [00:04<00:16,  3.38it/s]

49.9344476607515
1

Analysed test_samples from:  22%|████████████                                          | 16/72 [00:04<00:16,  3.42it/s]

50.79100638119145
1

Analysed test_samples from:  24%|████████████▊                                         | 17/72 [00:05<00:15,  3.60it/s]

49.97866797190333
1

Analysed test_samples from:  25%|█████████████▌                                        | 18/72 [00:05<00:14,  3.67it/s]

49.93279515215053
1

Analysed test_samples from:  26%|██████████████▎                                       | 19/72 [00:05<00:14,  3.67it/s]

50.487365395534
1

Analysed test_samples from:  28%|███████████████                                       | 20/72 [00:06<00:14,  3.71it/s]

48.490592019215754
1

Analysed test_samples from:  29%|███████████████▊                                      | 21/72 [00:06<00:13,  3.70it/s]

48.341795820833795
1

Analysed test_samples from:  31%|████████████████▌                                     | 22/72 [00:06<00:13,  3.70it/s]

47.665369471405114
1

Analysed test_samples from:  32%|█████████████████▎                                    | 23/72 [00:06<00:12,  3.79it/s]

49.90286331609413
1

Analysed test_samples from:  33%|██████████████████                                    | 24/72 [00:07<00:12,  3.72it/s]

51.746959805386325
1

Analysed test_samples from:  35%|██████████████████▊                                   | 25/72 [00:07<00:12,  3.70it/s]

51.34489200280452
1

Analysed test_samples from:  36%|███████████████████▌                                  | 26/72 [00:07<00:12,  3.67it/s]

49.27826461388156
1

Analysed test_samples from:  38%|████████████████████▎                                 | 27/72 [00:07<00:12,  3.65it/s]

46.4605339552424
1

Analysed test_samples from:  39%|█████████████████████                                 | 28/72 [00:08<00:12,  3.62it/s]

47.95628874844493
1

Analysed test_samples from:  40%|█████████████████████▊                                | 29/72 [00:08<00:11,  3.61it/s]

53.56417949204265
1

Analysed test_samples from:  42%|██████████████████████▌                               | 30/72 [00:08<00:11,  3.64it/s]

51.64631315907371
1

Analysed test_samples from:  43%|███████████████████████▎                              | 31/72 [00:09<00:11,  3.64it/s]

50.91423062183446
1

Analysed test_samples from:  44%|████████████████████████                              | 32/72 [00:09<00:10,  3.65it/s]

50.98927170052613
1

Analysed test_samples from:  46%|████████████████████████▊                             | 33/72 [00:09<00:10,  3.67it/s]

48.729831274915355
1

Analysed test_samples from:  47%|█████████████████████████▌                            | 34/72 [00:09<00:10,  3.61it/s]

52.90933255062059
1

Analysed test_samples from:  49%|██████████████████████████▎                           | 35/72 [00:10<00:10,  3.54it/s]

44.833699823752355
1

Analysed test_samples from:  50%|███████████████████████████                           | 36/72 [00:10<00:10,  3.58it/s]

40.191641441204226
1

Analysed test_samples from:  51%|███████████████████████████▋                          | 37/72 [00:10<00:09,  3.59it/s]

43.49328744124696
1

Analysed test_samples from:  53%|████████████████████████████▌                         | 38/72 [00:10<00:09,  3.66it/s]

49.11095497936694
1

Analysed test_samples from:  54%|█████████████████████████████▏                        | 39/72 [00:11<00:08,  3.75it/s]

45.00505099955254
1

Analysed test_samples from:  56%|██████████████████████████████                        | 40/72 [00:11<00:09,  3.37it/s]

41.707761754281115
1

Analysed test_samples from:  57%|██████████████████████████████▊                       | 41/72 [00:11<00:09,  3.21it/s]

39.191946516891136
1

Analysed test_samples from:  58%|███████████████████████████████▌                      | 42/72 [00:12<00:09,  3.05it/s]

38.288667040632426
1

Analysed test_samples from:  60%|████████████████████████████████▎                     | 43/72 [00:12<00:08,  3.24it/s]

37.868587392876
1

Analysed test_samples from:  61%|█████████████████████████████████                     | 44/72 [00:12<00:08,  3.16it/s]

45.85076541164648
1

Analysed test_samples from:  62%|█████████████████████████████████▊                    | 45/72 [00:13<00:08,  3.26it/s]

46.22620817195936
1

Analysed test_samples from:  64%|██████████████████████████████████▌                   | 46/72 [00:13<00:07,  3.35it/s]

45.51218617383205
1

Analysed test_samples from:  65%|███████████████████████████████████▎                  | 47/72 [00:13<00:07,  3.40it/s]

43.891413699276185
1

Analysed test_samples from:  67%|████████████████████████████████████                  | 48/72 [00:14<00:07,  3.13it/s]

46.64817171204582
1

Analysed test_samples from:  68%|████████████████████████████████████▊                 | 49/72 [00:14<00:07,  3.27it/s]

48.89402277093959
1

Analysed test_samples from:  69%|█████████████████████████████████████▌                | 50/72 [00:14<00:06,  3.27it/s]

49.627645195047776
1

Analysed test_samples from:  71%|██████████████████████████████████████▎               | 51/72 [00:14<00:06,  3.37it/s]

49.21052560504836
1

Analysed test_samples from:  72%|███████████████████████████████████████               | 52/72 [00:15<00:05,  3.49it/s]

49.370924713732194
1

Analysed test_samples from:  74%|███████████████████████████████████████▊              | 53/72 [00:15<00:05,  3.25it/s]

51.256687660737
1

Analysed test_samples from:  75%|████████████████████████████████████████▌             | 54/72 [00:15<00:06,  3.00it/s]

47.82868295651171
1

Analysed test_samples from:  76%|█████████████████████████████████████████▎            | 55/72 [00:16<00:05,  3.15it/s]

45.91721955803389
1

Analysed test_samples from:  78%|██████████████████████████████████████████            | 56/72 [00:16<00:05,  3.08it/s]

46.53768062389028
1

Analysed test_samples from:  79%|██████████████████████████████████████████▊           | 57/72 [00:16<00:05,  2.93it/s]

51.00293851896077
1

Analysed test_samples from:  81%|███████████████████████████████████████████▌          | 58/72 [00:17<00:04,  2.90it/s]

50.032123240027886
1

Analysed test_samples from:  82%|████████████████████████████████████████████▎         | 59/72 [00:17<00:04,  3.15it/s]

51.11488389619551
1

Analysed test_samples from:  83%|█████████████████████████████████████████████         | 60/72 [00:17<00:03,  3.27it/s]

50.84511096396315
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 61/72 [00:18<00:03,  3.34it/s]

49.16440782718508
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 62/72 [00:18<00:03,  3.09it/s]

51.80241246433608
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 63/72 [00:18<00:02,  3.01it/s]

50.754896851393795
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 64/72 [00:19<00:02,  2.93it/s]

52.227575757606246
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 65/72 [00:19<00:02,  3.11it/s]

56.44374137895359
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 66/72 [00:19<00:01,  3.19it/s]

54.92519439958987
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 67/72 [00:20<00:01,  3.29it/s]

47.32231620660458
1

Analysed test_samples from:  94%|███████████████████████████████████████████████████   | 68/72 [00:20<00:01,  3.34it/s]

50.66854982755782
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 69/72 [00:20<00:00,  3.33it/s]

49.90038885031902
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 70/72 [00:20<00:00,  3.37it/s]

49.64257022854768
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 71/72 [00:21<00:00,  3.35it/s]

50.12571523285525
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 72/72 [00:21<00:00,  3.34it/s]

48.21790834734276
False
False

Madryd model selection:   0%|                                                                   | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Madryd model selection:   4%|██▏                                                        | 1/27 [00:00<00:08,  3.08it/s]

1
Order: (0, 0, 0), AICc: 5095.04, ADF p-value: 0.1031, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 0, 1)
2

Madryd model selection:   7%|████▎                                                      | 2/27 [00:00<00:06,  3.77it/s]

1
Order: (0, 0, 1), AICc: 4498.82, ADF p-value: 0.0997, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 0, 2)
2

Madryd model selection:  15%|████████▋                                                  | 4/27 [00:00<00:05,  4.39it/s]

1
Order: (0, 0, 2), AICc: 4223.54, ADF p-value: 0.1386, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 1, 0)
2
1
Order: (0, 1, 0), AICc: 3374.05, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3374.05
(0, 1, 1)
2

Madryd model selection:  19%|██████████▉                                                | 5/27 [00:01<00:04,  4.75it/s]

1
Order: (0, 1, 1), AICc: 3371.65, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3371.65
(0, 1, 2)
2
1
Order: (0, 1, 2), AICc: 3345.93, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93

Madryd model selection:  22%|█████████████                                              | 6/27 [00:01<00:04,  5.09it/s]

(0, 2, 0)
2

Madryd model selection:  26%|███████████████▎                                           | 7/27 [00:01<00:04,  4.81it/s]

1
Order: (0, 2, 0), AICc: 3810.48, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(0, 2, 1)
2

Madryd model selection:  30%|█████████████████▍                                         | 8/27 [00:01<00:04,  4.23it/s]

1
Order: (0, 2, 1), AICc: 3373.02, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(0, 2, 2)
2

Madryd model selection:  33%|███████████████████▋                                       | 9/27 [00:02<00:05,  3.03it/s]

1
Order: (0, 2, 2), AICc: 3370.80, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(1, 0, 0)
2

Madryd model selection:  37%|█████████████████████▍                                    | 10/27 [00:02<00:05,  3.11it/s]

1
Order: (1, 0, 0), AICc: 3379.75, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(1, 0, 1)
2

Madryd model selection:  41%|███████████████████████▋                                  | 11/27 [00:03<00:05,  2.81it/s]

1
Order: (1, 0, 1), AICc: 3377.52, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(1, 0, 2)
2

Madryd model selection:  48%|███████████████████████████▉                              | 13/27 [00:03<00:04,  2.95it/s]

1
Order: (1, 0, 2), AICc: 3352.60, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3376.00, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(1, 1, 1)
2

Madryd model selection:  52%|██████████████████████████████                            | 14/27 [00:04<00:04,  2.95it/s]

1
Order: (1, 1, 1), AICc: 3349.29, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3345.93
(1, 1, 2)
2

Madryd model selection:  56%|████████████████████████████████▏                         | 15/27 [00:04<00:04,  2.99it/s]

1
Order: (1, 1, 2), AICc: 3331.21, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(1, 2, 0)
2
1
Order: (1, 2, 0), AICc: 3695.81, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21

Madryd model selection:  59%|██████████████████████████████████▎                       | 16/27 [00:04<00:03,  3.47it/s]

(1, 2, 1)
2

Madryd model selection:  63%|████████████████████████████████████▌                     | 17/27 [00:05<00:03,  2.89it/s]

1
Order: (1, 2, 1), AICc: 3375.06, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(1, 2, 2)
2

Madryd model selection:  67%|██████████████████████████████████████▋                   | 18/27 [00:05<00:04,  2.24it/s]

1
Order: (1, 2, 2), AICc: 3368.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 0, 0)
2

Madryd model selection:  70%|████████████████████████████████████████▊                 | 19/27 [00:06<00:03,  2.22it/s]

1
Order: (2, 0, 0), AICc: 3377.58, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 0, 1)
2

Madryd model selection:  74%|██████████████████████████████████████████▉               | 20/27 [00:06<00:03,  2.03it/s]

1
Order: (2, 0, 1), AICc: 3549.70, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 0, 2)
2

Madryd model selection:  78%|█████████████████████████████████████████████             | 21/27 [00:07<00:03,  1.84it/s]

1
Order: (2, 0, 2), AICc: 3338.69, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 1, 0)
2

Madryd model selection:  81%|███████████████████████████████████████████████▎          | 22/27 [00:07<00:02,  2.24it/s]

1
Order: (2, 1, 0), AICc: 3355.02, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 1, 1)
2

Madryd model selection:  85%|█████████████████████████████████████████████████▍        | 23/27 [00:08<00:01,  2.52it/s]

1
Order: (2, 1, 1), AICc: 3335.71, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 1, 2)
2

Madryd model selection:  89%|███████████████████████████████████████████████████▌      | 24/27 [00:08<00:01,  2.12it/s]

1
Order: (2, 1, 2), AICc: 3333.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 2, 0)
2

Madryd model selection:  93%|█████████████████████████████████████████████████████▋    | 25/27 [00:08<00:00,  2.49it/s]

1
Order: (2, 2, 0), AICc: 3607.15, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 2, 1)
2

Madryd model selection:  96%|███████████████████████████████████████████████████████▊  | 26/27 [00:09<00:00,  2.39it/s]

1
Order: (2, 2, 1), AICc: 3359.43, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21
(2, 2, 2)
2

Madryd model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:10<00:00,  2.66it/s]

1
Order: (2, 2, 2), AICc: 3336.45, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3331.21

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:21,  3.29it/s]

56.84418113471926
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:23,  3.01it/s]

57.16715138817983
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:00<00:23,  3.02it/s]

59.097220624308875
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:21,  3.27it/s]

60.75379985784121
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:01<00:22,  2.98it/s]

60.1257459276806
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:01<00:22,  3.00it/s]

62.14833183493873
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:02<00:20,  3.26it/s]

62.47077017817846
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:02<00:20,  3.17it/s]

60.624302400465396
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:02<00:20,  3.07it/s]

61.19734856503432
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:03<00:19,  3.15it/s]

61.6304267640063
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:03<00:19,  3.19it/s]

60.356623087134395
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:03<00:19,  3.07it/s]

59.663081812355436
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:04<00:18,  3.30it/s]

60.99623026795676
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:04<00:16,  3.50it/s]

60.022173693887396
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:04<00:17,  3.28it/s]

59.22142439365415
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:05<00:17,  3.18it/s]

60.956410329595734
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:05<00:17,  3.15it/s]

59.35920481809927
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:05<00:17,  3.08it/s]

57.897130293141586
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:06<00:18,  2.98it/s]

57.91181655449001
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:06<00:18,  2.92it/s]

58.57593007465846
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:06<00:17,  2.95it/s]

59.740732692310324
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:07<00:17,  2.85it/s]

59.2066686689232
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:07<00:17,  2.81it/s]

62.29815404060058
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:07<00:17,  2.82it/s]

61.96870036579729
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:08<00:16,  2.84it/s]

62.467167621348054
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:08<00:16,  2.84it/s]

61.18361314470391
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:08<00:16,  2.80it/s]

60.99303884735693
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:09<00:16,  2.81it/s]

60.877802337963054
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:09<00:15,  2.83it/s]

61.336218837757926
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:10<00:15,  2.78it/s]

60.18423355878912
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:10<00:15,  2.75it/s]

59.87224579037867
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:10<00:14,  2.75it/s]

59.838667189775144
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:11<00:14,  2.78it/s]

57.884706277835214
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:11<00:13,  2.85it/s]

57.61254346182554
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:11<00:13,  2.79it/s]

55.46230265933541
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:12<00:13,  2.75it/s]

56.756481137571754
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:12<00:13,  2.77it/s]

57.52076132958234
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:12<00:13,  2.69it/s]

57.56261898497799
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:13<00:12,  2.72it/s]

58.45108601827787
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:13<00:12,  2.73it/s]

59.84931491851994
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:14<00:11,  2.73it/s]

61.32951749625459
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:14<00:11,  2.71it/s]

61.97453924171311
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:14<00:11,  2.71it/s]

58.31337645565895
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:15<00:10,  2.66it/s]

56.840174653998645
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:15<00:10,  2.64it/s]

56.739566383010015
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:15<00:10,  2.60it/s]

58.57676288092152
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:16<00:09,  2.60it/s]

56.88614944998699
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:16<00:09,  2.63it/s]

57.19377713085008
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:17<00:09,  2.60it/s]

57.46232968474985
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:17<00:08,  2.60it/s]

58.19531422264831
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:17<00:08,  2.61it/s]

59.198328672762656
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:18<00:08,  2.59it/s]

60.75800930543049
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:18<00:07,  2.59it/s]

60.39154788597341
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:19<00:07,  2.51it/s]

60.16961846270894
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:19<00:07,  2.53it/s]

58.26359698915964
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:19<00:06,  2.55it/s]

56.785447633840896
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:20<00:06,  2.54it/s]

57.683850947144265
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:20<00:05,  2.51it/s]

56.377182361498654
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:21<00:05,  2.51it/s]

56.25990709559484
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:21<00:05,  2.51it/s]

56.042151058397835
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:21<00:04,  2.54it/s]

56.21599181112341
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:22<00:04,  2.55it/s]

56.49468781219472
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:22<00:03,  2.54it/s]

56.921693822378856
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:22<00:03,  2.55it/s]

56.95518828182627
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:23<00:03,  2.54it/s]

56.48610853898042
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:23<00:02,  2.53it/s]

56.24109646413762
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:24<00:02,  2.55it/s]

56.084035832195454
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:24<00:01,  2.54it/s]

55.581455849344394
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:24<00:01,  2.53it/s]

55.64857859917841
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:25<00:01,  2.56it/s]

55.90829478334101
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:25<00:00,  2.58it/s]

56.825451952760645
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:26<00:00,  2.56it/s]

56.84434568183184
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:26<00:00,  2.75it/s]

58.51117650186915
False
False

Moskwa model selection:   4%|██▏                                                        | 1/27 [00:00<00:03,  7.28it/s]

(0, 0, 0)
2
1
Order: (0, 0, 0), AICc: 1706.80, ADF p-value: 0.0199, Best_Order: (0, 0, 0), Best_aicc: 1706.80
(0, 0, 1)
2

Moskwa model selection:   7%|████▎                                                      | 2/27 [00:00<00:04,  5.34it/s]

1
Order: (0, 0, 1), AICc: 1515.47, ADF p-value: 0.0343, Best_Order: (0, 0, 1), Best_aicc: 1515.47
(0, 0, 2)
2

Moskwa model selection:  15%|████████▋                                                  | 4/27 [00:00<00:04,  5.34it/s]

1
Order: (0, 0, 2), AICc: 1484.24, ADF p-value: 0.0768, Best_Order: (0, 0, 1), Best_aicc: 1515.47
(0, 1, 0)
2
1
Order: (0, 1, 0), AICc: 1310.84, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 1310.84
(0, 1, 1)
2

Moskwa model selection:  19%|██████████▉                                                | 5/27 [00:00<00:04,  4.91it/s]

1
Order: (0, 1, 1), AICc: 1295.58, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 1295.58
(0, 1, 2)
2

Moskwa model selection:  26%|███████████████▎                                           | 7/27 [00:01<00:03,  5.22it/s]

1
Order: (0, 1, 2), AICc: 1267.18, ADF p-value: 0.0023, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(0, 2, 0)
2
1
Order: (0, 2, 0), AICc: 1524.30, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(0, 2, 1)
2

Moskwa model selection:  30%|█████████████████▍                                         | 8/27 [00:01<00:03,  5.29it/s]

1
Order: (0, 2, 1), AICc: 1309.60, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(0, 2, 2)
2

Moskwa model selection:  33%|███████████████████▋                                       | 9/27 [00:01<00:03,  4.57it/s]

1
Order: (0, 2, 2), AICc: 1294.81, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 0, 0)
2
1
Order: (1, 0, 0), AICc: 1318.21, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18

Moskwa model selection:  37%|█████████████████████▍                                    | 10/27 [00:02<00:03,  4.69it/s]

(1, 0, 1)
2

Moskwa model selection:  41%|███████████████████████▋                                  | 11/27 [00:02<00:03,  4.44it/s]

1
Order: (1, 0, 1), AICc: 1303.80, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 0, 2)
2

Moskwa model selection:  48%|███████████████████████████▉                              | 13/27 [00:02<00:02,  4.81it/s]

1
Order: (1, 0, 2), AICc: 1277.42, ADF p-value: 0.0036, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 1306.65, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 1, 1)
2

Moskwa model selection:  52%|██████████████████████████████                            | 14/27 [00:02<00:02,  4.57it/s]

1
Order: (1, 1, 1), AICc: 1277.40, ADF p-value: 0.0007, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 1, 2)
2

Moskwa model selection:  59%|██████████████████████████████████▎                       | 16/27 [00:03<00:02,  4.97it/s]

1
Order: (1, 1, 2), AICc: 1267.32, ADF p-value: 0.0011, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 2, 0)
2
1
Order: (1, 2, 0), AICc: 1462.77, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 2, 1)
2

Moskwa model selection:  63%|████████████████████████████████████▌                     | 17/27 [00:03<00:02,  4.40it/s]

1
Order: (1, 2, 1), AICc: 1305.84, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(1, 2, 2)
2

Moskwa model selection:  67%|██████████████████████████████████████▋                   | 18/27 [00:03<00:02,  4.05it/s]

1
Order: (1, 2, 2), AICc: 1303.74, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(2, 0, 0)
2

Moskwa model selection:  70%|████████████████████████████████████████▊                 | 19/27 [00:04<00:01,  4.11it/s]

1
Order: (2, 0, 0), AICc: 1311.87, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(2, 0, 1)
2

Moskwa model selection:  74%|██████████████████████████████████████████▉               | 20/27 [00:04<00:01,  3.75it/s]

1
Order: (2, 0, 1), AICc: 1304.40, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(2, 0, 2)
2

Moskwa model selection:  81%|███████████████████████████████████████████████▎          | 22/27 [00:04<00:01,  4.16it/s]

1
Order: (2, 0, 2), AICc: 1278.56, ADF p-value: 0.0022, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(2, 1, 0)
2
1
Order: (2, 1, 0), AICc: 1286.50, ADF p-value: 0.0001, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(2, 1, 1)
2

Moskwa model selection:  85%|█████████████████████████████████████████████████▍        | 23/27 [00:05<00:01,  3.77it/s]

1
Order: (2, 1, 1), AICc: 1277.30, ADF p-value: 0.0004, Best_Order: (0, 1, 2), Best_aicc: 1267.18
(2, 1, 2)
2

Moskwa model selection:  89%|███████████████████████████████████████████████████▌      | 24/27 [00:05<00:00,  3.55it/s]

1
Order: (2, 1, 2), AICc: 1264.30, ADF p-value: 0.0037, Best_Order: (2, 1, 2), Best_aicc: 1264.30
(2, 2, 0)
2
1
Order: (2, 2, 0), AICc: 1390.33, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 1264.30

Moskwa model selection:  93%|█████████████████████████████████████████████████████▋    | 25/27 [00:05<00:00,  3.91it/s]

(2, 2, 1)
2

Moskwa model selection:  96%|███████████████████████████████████████████████████████▊  | 26/27 [00:06<00:00,  3.53it/s]

1
Order: (2, 2, 1), AICc: 1290.55, ADF p-value: 0.0002, Best_Order: (2, 1, 2), Best_aicc: 1264.30
(2, 2, 2)
2

Moskwa model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:06<00:00,  4.16it/s]

1
Order: (2, 2, 2), AICc: 1284.15, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 1264.30

Analysed test_samples from:   0%|                                                               | 0/29 [00:00<?, ?it/s]

1

Analysed test_samples from:   3%|█▉                                                     | 1/29 [00:00<00:07,  3.51it/s]

71.18240566066339
1

Analysed test_samples from:   7%|███▊                                                   | 2/29 [00:00<00:07,  3.43it/s]

67.87308804432368
1

Analysed test_samples from:  10%|█████▋                                                 | 3/29 [00:00<00:07,  3.39it/s]

72.74953326462177
1

Analysed test_samples from:  14%|███████▌                                               | 4/29 [00:01<00:07,  3.26it/s]

65.67984875850792
1

Analysed test_samples from:  17%|█████████▍                                             | 5/29 [00:01<00:07,  3.30it/s]

64.42972463327679
1

Analysed test_samples from:  21%|███████████▍                                           | 6/29 [00:01<00:08,  2.85it/s]

61.885643418205646
1
62.03212734138276

Analysed test_samples from:  28%|███████████████▏                                       | 8/29 [00:02<00:05,  3.77it/s]

1
59.33133416779293
1

Analysed test_samples from:  31%|█████████████████                                      | 9/29 [00:02<00:04,  4.14it/s]

67.89796853476662
1

Analysed test_samples from:  34%|██████████████████▌                                   | 10/29 [00:02<00:04,  3.83it/s]

66.9392621640329
1

Analysed test_samples from:  38%|████████████████████▍                                 | 11/29 [00:03<00:04,  3.81it/s]

62.74877282124183
1

Analysed test_samples from:  41%|██████████████████████▎                               | 12/29 [00:03<00:04,  3.83it/s]

65.83984872557342
1

Analysed test_samples from:  45%|████████████████████████▏                             | 13/29 [00:03<00:04,  3.67it/s]

70.76848351523398
1

Analysed test_samples from:  48%|██████████████████████████                            | 14/29 [00:03<00:04,  3.60it/s]

70.48743080762776
1

Analysed test_samples from:  52%|███████████████████████████▉                          | 15/29 [00:04<00:04,  3.46it/s]

66.240860397198
1

Analysed test_samples from:  55%|█████████████████████████████▊                        | 16/29 [00:04<00:04,  2.95it/s]

57.590116875342176
1

Analysed test_samples from:  62%|█████████████████████████████████▌                    | 18/29 [00:05<00:03,  3.15it/s]

53.296666680057086
1
54.1855151157625
1

Analysed test_samples from:  66%|███████████████████████████████████▍                  | 19/29 [00:05<00:03,  3.21it/s]

52.75678947228517
1

Analysed test_samples from:  69%|█████████████████████████████████████▏                | 20/29 [00:05<00:02,  3.23it/s]

49.74949739582817
1

Analysed test_samples from:  72%|███████████████████████████████████████               | 21/29 [00:06<00:02,  3.31it/s]

56.829466298528864
1

Analysed test_samples from:  76%|████████████████████████████████████████▉             | 22/29 [00:06<00:02,  2.96it/s]

26.862423767446607
1

Analysed test_samples from:  79%|██████████████████████████████████████████▊           | 23/29 [00:06<00:01,  3.04it/s]

31.195937694899754
1

Analysed test_samples from:  83%|████████████████████████████████████████████▋         | 24/29 [00:07<00:01,  3.17it/s]

31.48386798577277
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 25/29 [00:07<00:01,  3.36it/s]

45.85482495189922
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▍     | 26/29 [00:07<00:00,  3.05it/s]

38.028273760415296
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 27/29 [00:08<00:00,  3.13it/s]

36.74738064284894
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▏ | 28/29 [00:08<00:00,  3.35it/s]

28.459344895305797
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 29/29 [00:08<00:00,  3.33it/s]

40.276240074417274
False
False

Paryż model selection:   0%|                                                                    | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Paryż model selection:   4%|██▏                                                         | 1/27 [00:00<00:07,  3.25it/s]

1
Order: (0, 0, 0), AICc: 4085.56, ADF p-value: 0.0793, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 0, 1)
2

Paryż model selection:   7%|████▍                                                       | 2/27 [00:00<00:06,  3.80it/s]

1
Order: (0, 0, 1), AICc: 3714.64, ADF p-value: 0.0523, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 0, 2)
2

Paryż model selection:  11%|██████▋                                                     | 3/27 [00:00<00:06,  3.56it/s]

1
Order: (0, 0, 2), AICc: 3565.38, ADF p-value: 0.0879, Best_Order: (0, 0, 0), Best_aicc: inf
(0, 1, 0)
2

Paryż model selection:  15%|████████▉                                                   | 4/27 [00:01<00:05,  3.89it/s]

1
Order: (0, 1, 0), AICc: 3321.62, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3321.62
(0, 1, 1)
2
1
Order: (0, 1, 1), AICc: 3310.77, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3310.77

Paryż model selection:  19%|███████████                                                 | 5/27 [00:01<00:04,  4.48it/s]

(0, 1, 2)
2
1

Paryż model selection:  22%|█████████████▎                                              | 6/27 [00:01<00:04,  4.52it/s]

Order: (0, 1, 2), AICc: 3255.19, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(0, 2, 0)
2

Paryż model selection:  26%|███████████████▌                                            | 7/27 [00:01<00:04,  4.45it/s]

1
Order: (0, 2, 0), AICc: 3810.11, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(0, 2, 1)
2

Paryż model selection:  30%|█████████████████▊                                          | 8/27 [00:02<00:05,  3.50it/s]

1
Order: (0, 2, 1), AICc: 3321.69, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(0, 2, 2)
2

Paryż model selection:  33%|████████████████████                                        | 9/27 [00:02<00:06,  2.71it/s]

1
Order: (0, 2, 2), AICc: 3310.21, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(1, 0, 0)
2

Paryż model selection:  37%|█████████████████████▊                                     | 10/27 [00:02<00:05,  2.93it/s]

1
Order: (1, 0, 0), AICc: 3327.17, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(1, 0, 1)
2

Paryż model selection:  41%|████████████████████████                                   | 11/27 [00:03<00:06,  2.61it/s]

1
Order: (1, 0, 1), AICc: 3315.27, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(1, 0, 2)
2

Paryż model selection:  48%|████████████████████████████▍                              | 13/27 [00:04<00:04,  2.85it/s]

1
Order: (1, 0, 2), AICc: 3278.54, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3319.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(1, 1, 1)
2

Paryż model selection:  52%|██████████████████████████████▌                            | 14/27 [00:04<00:04,  2.99it/s]

1
Order: (1, 1, 1), AICc: 3255.50, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3255.19
(1, 1, 2)
2

Paryż model selection:  56%|████████████████████████████████▊                          | 15/27 [00:04<00:04,  2.93it/s]

1
Order: (1, 1, 2), AICc: 3243.38, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(1, 2, 0)
2
1

Paryż model selection:  59%|██████████████████████████████████▉                        | 16/27 [00:04<00:03,  3.34it/s]

Order: (1, 2, 0), AICc: 3691.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(1, 2, 1)
2

Paryż model selection:  63%|█████████████████████████████████████▏                     | 17/27 [00:05<00:03,  3.05it/s]

1
Order: (1, 2, 1), AICc: 3319.52, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(1, 2, 2)
2

Paryż model selection:  67%|███████████████████████████████████████▎                   | 18/27 [00:05<00:03,  2.41it/s]

1
Order: (1, 2, 2), AICc: 3320.39, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 0, 0)
2

Paryż model selection:  70%|█████████████████████████████████████████▌                 | 19/27 [00:06<00:03,  2.40it/s]

1
Order: (2, 0, 0), AICc: 3319.81, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 0, 1)
2

Paryż model selection:  74%|███████████████████████████████████████████▋               | 20/27 [00:07<00:03,  2.04it/s]

1
Order: (2, 0, 1), AICc: 3350.31, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 0, 2)
2

Paryż model selection:  78%|█████████████████████████████████████████████▉             | 21/27 [00:07<00:03,  1.87it/s]

1
Order: (2, 0, 2), AICc: 3294.69, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 1, 0)
2

Paryż model selection:  81%|████████████████████████████████████████████████           | 22/27 [00:07<00:02,  2.27it/s]

1
Order: (2, 1, 0), AICc: 3275.46, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 1, 1)
2

Paryż model selection:  85%|██████████████████████████████████████████████████▎        | 23/27 [00:08<00:01,  2.39it/s]

1
Order: (2, 1, 1), AICc: 3250.18, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 1, 2)
2

Paryż model selection:  89%|████████████████████████████████████████████████████▍      | 24/27 [00:08<00:01,  2.31it/s]

1
Order: (2, 1, 2), AICc: 3244.84, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 2, 0)
2

Paryż model selection:  93%|██████████████████████████████████████████████████████▋    | 25/27 [00:08<00:00,  2.70it/s]

1
Order: (2, 2, 0), AICc: 3570.80, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 2, 1)
2

Paryż model selection:  96%|████████████████████████████████████████████████████████▊  | 26/27 [00:09<00:00,  2.40it/s]

1
Order: (2, 2, 1), AICc: 3280.02, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38
(2, 2, 2)
2

Paryż model selection: 100%|███████████████████████████████████████████████████████████| 27/27 [00:10<00:00,  2.63it/s]

1
Order: (2, 2, 2), AICc: 3253.56, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3243.38

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:23,  3.02it/s]

61.47314607721131
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:22,  3.20it/s]

59.37122706610978
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:00<00:21,  3.26it/s]

59.57556375891001
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:20,  3.43it/s]

57.85456104979216
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:01<00:19,  3.45it/s]

58.57114000977831
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:01<00:19,  3.43it/s]

59.68496235390461
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:02<00:18,  3.50it/s]

58.11385099625953
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:02<00:18,  3.47it/s]

59.54910711588671
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:02<00:18,  3.55it/s]

60.05973587768145
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:02<00:17,  3.55it/s]

59.65100903118713
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:03<00:17,  3.56it/s]

58.34824261453546
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:03<00:17,  3.53it/s]

59.71294921412107
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:03<00:16,  3.54it/s]

60.416479070953116
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:04<00:17,  3.46it/s]

56.39383192904149
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:04<00:16,  3.46it/s]

56.35777653408947
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:04<00:16,  3.53it/s]

56.752340912013025
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:04<00:15,  3.52it/s]

57.47056855412603
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:05<00:15,  3.53it/s]

58.12084236330804
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:05<00:15,  3.55it/s]

57.10130136484926
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:05<00:15,  3.50it/s]

58.047119136905636
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:06<00:14,  3.47it/s]

57.34921825095911
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:06<00:14,  3.46it/s]

56.20151514248222
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:06<00:14,  3.49it/s]

55.77539227366583
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:06<00:14,  3.45it/s]

54.975831260389796
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:07<00:13,  3.44it/s]

61.05578959231484
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:07<00:13,  3.42it/s]

59.6442659348958
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:07<00:13,  3.35it/s]

58.25580805988456
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:08<00:13,  3.40it/s]

54.64395934124299
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:08<00:12,  3.44it/s]

56.27822350032338
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:08<00:12,  3.48it/s]

58.09728033560655
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:08<00:11,  3.51it/s]

59.6086100437044
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:09<00:11,  3.58it/s]

57.842454922042315
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:09<00:11,  3.57it/s]

58.398655792368345
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:09<00:10,  3.59it/s]

53.3027844156787
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:10<00:10,  3.47it/s]

57.857356070503016
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:10<00:10,  3.37it/s]

55.152163644475635
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:10<00:10,  3.31it/s]

51.653922997909085
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:11<00:10,  3.31it/s]

51.70665326238125
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:11<00:10,  3.29it/s]

52.82897906760032
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:11<00:09,  3.32it/s]

52.305460120212345
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:11<00:09,  3.35it/s]

52.70076108574168
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:12<00:09,  3.34it/s]

51.6020494370417
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:12<00:09,  3.32it/s]

46.4320522975593
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:12<00:08,  3.33it/s]

44.3606659600579
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:13<00:08,  3.30it/s]

45.838866343539166
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:13<00:08,  3.37it/s]

52.70191677284376
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:13<00:07,  3.37it/s]

54.69473670886483
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:14<00:07,  3.29it/s]

52.97670794991379
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:14<00:07,  3.26it/s]

50.93725168282195
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:14<00:07,  3.24it/s]

54.25857432858504
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:14<00:06,  3.24it/s]

54.60237244814358
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:15<00:06,  3.31it/s]

56.703497906110094
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:15<00:05,  3.34it/s]

58.202913098882775
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:15<00:05,  3.35it/s]

58.85553404630832
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:16<00:05,  3.38it/s]

56.79942868446271
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:16<00:05,  3.34it/s]

55.711252378268085
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:16<00:04,  3.33it/s]

55.55257388178002
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:17<00:04,  3.33it/s]

53.2984253437562
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:17<00:04,  3.32it/s]

46.67938026896452
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Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:17<00:03,  3.36it/s]

47.53054149655809
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:17<00:03,  3.32it/s]

53.10397305904415
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:18<00:03,  3.15it/s]

56.58415728157573
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:18<00:03,  3.11it/s]

57.48088811837981
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:18<00:03,  2.97it/s]

57.586187849726336
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:19<00:02,  3.07it/s]

57.95494585964413
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:19<00:02,  2.93it/s]

58.102754257703396
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:20<00:02,  2.89it/s]

59.07990652307736
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:20<00:01,  2.97it/s]

57.473061219356126
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:20<00:01,  2.97it/s]

55.78413566742749
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:20<00:00,  3.08it/s]

58.78916213926437
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:21<00:00,  3.14it/s]

58.698240967903956
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:21<00:00,  3.18it/s]

57.945053286398156
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:21<00:00,  3.33it/s]

55.728440492325696
False
False

Praga model selection:   0%|                                                                    | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Praga model selection:   4%|██▏                                                         | 1/27 [00:00<00:06,  4.05it/s]

1
Order: (0, 0, 0), AICc: 4023.48, ADF p-value: 0.0057, Best_Order: (0, 0, 0), Best_aicc: 4023.48
(0, 0, 1)
2

Praga model selection:   7%|████▍                                                       | 2/27 [00:00<00:06,  3.66it/s]

1
Order: (0, 0, 1), AICc: 3730.51, ADF p-value: 0.0458, Best_Order: (0, 0, 1), Best_aicc: 3730.51
(0, 0, 2)
2

Praga model selection:  11%|██████▋                                                     | 3/27 [00:00<00:07,  3.32it/s]

1
Order: (0, 0, 2), AICc: 3651.67, ADF p-value: 0.0037, Best_Order: (0, 0, 2), Best_aicc: 3651.67
(0, 1, 0)
2

Praga model selection:  15%|████████▉                                                   | 4/27 [00:01<00:06,  3.58it/s]

1
Order: (0, 1, 0), AICc: 3432.74, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3432.74
(0, 1, 1)
2
1
Order: (0, 1, 1), AICc: 3393.72, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3393.72

Praga model selection:  19%|███████████                                                 | 5/27 [00:01<00:05,  4.18it/s]

(0, 1, 2)
2

Praga model selection:  22%|█████████████▎                                              | 6/27 [00:01<00:05,  4.00it/s]

1
Order: (0, 1, 2), AICc: 3352.52, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(0, 2, 0)
2

Praga model selection:  26%|███████████████▌                                            | 7/27 [00:01<00:04,  4.22it/s]

1
Order: (0, 2, 0), AICc: 3947.67, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(0, 2, 1)
2

Praga model selection:  30%|█████████████████▊                                          | 8/27 [00:02<00:05,  3.32it/s]

1
Order: (0, 2, 1), AICc: 3431.92, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(0, 2, 2)
2

Praga model selection:  33%|████████████████████                                        | 9/27 [00:02<00:07,  2.55it/s]

1
Order: (0, 2, 2), AICc: 3393.26, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(1, 0, 0)
2

Praga model selection:  37%|█████████████████████▊                                     | 10/27 [00:03<00:06,  2.76it/s]

1
Order: (1, 0, 0), AICc: 3436.75, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(1, 0, 1)
2

Praga model selection:  41%|████████████████████████                                   | 11/27 [00:03<00:06,  2.50it/s]

1
Order: (1, 0, 1), AICc: 3399.29, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(1, 0, 2)
2

Praga model selection:  48%|████████████████████████████▍                              | 13/27 [00:04<00:05,  2.80it/s]

1
Order: (1, 0, 2), AICc: 3358.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3418.12, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3352.52
(1, 1, 1)
2

Praga model selection:  52%|██████████████████████████████▌                            | 14/27 [00:04<00:04,  2.91it/s]

1
Order: (1, 1, 1), AICc: 3350.67, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3350.67
(1, 1, 2)
2

Praga model selection:  56%|████████████████████████████████▊                          | 15/27 [00:04<00:04,  2.88it/s]

1
Order: (1, 1, 2), AICc: 3347.47, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(1, 2, 0)
2
1

Praga model selection:  59%|██████████████████████████████████▉                        | 16/27 [00:05<00:03,  3.30it/s]

Order: (1, 2, 0), AICc: 3801.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(1, 2, 1)
2

Praga model selection:  63%|█████████████████████████████████████▏                     | 17/27 [00:05<00:03,  2.74it/s]

1
Order: (1, 2, 1), AICc: 3417.41, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(1, 2, 2)
2

Praga model selection:  67%|███████████████████████████████████████▎                   | 18/27 [00:06<00:04,  2.17it/s]

1
Order: (1, 2, 2), AICc: 3352.15, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 0, 0)
2

Praga model selection:  70%|█████████████████████████████████████████▌                 | 19/27 [00:06<00:03,  2.15it/s]

1
Order: (2, 0, 0), AICc: 3418.03, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 0, 1)
2

Praga model selection:  74%|███████████████████████████████████████████▋               | 20/27 [00:07<00:03,  2.07it/s]

1
Order: (2, 0, 1), AICc: 3365.36, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 0, 2)
2

Praga model selection:  78%|█████████████████████████████████████████████▉             | 21/27 [00:07<00:03,  1.89it/s]

1
Order: (2, 0, 2), AICc: 3358.93, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 1, 0)
2

Praga model selection:  81%|████████████████████████████████████████████████           | 22/27 [00:08<00:02,  2.24it/s]

1
Order: (2, 1, 0), AICc: 3382.31, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 1, 1)
2

Praga model selection:  85%|██████████████████████████████████████████████████▎        | 23/27 [00:08<00:01,  2.40it/s]

1
Order: (2, 1, 1), AICc: 3352.53, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 1, 2)
2

Praga model selection:  89%|████████████████████████████████████████████████████▍      | 24/27 [00:09<00:01,  2.27it/s]

1
Order: (2, 1, 2), AICc: 3348.48, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 2, 0)
2

Praga model selection:  93%|██████████████████████████████████████████████████████▋    | 25/27 [00:09<00:00,  2.63it/s]

1
Order: (2, 2, 0), AICc: 3676.88, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 2, 1)
2

Praga model selection:  96%|████████████████████████████████████████████████████████▊  | 26/27 [00:09<00:00,  2.21it/s]

1
Order: (2, 2, 1), AICc: 3386.71, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47
(2, 2, 2)
2

Praga model selection: 100%|███████████████████████████████████████████████████████████| 27/27 [00:10<00:00,  2.53it/s]

1
Order: (2, 2, 2), AICc: 3355.86, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3347.47

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:24,  2.97it/s]

49.00555036304587
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:25,  2.84it/s]

52.52454547440489
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:01<00:25,  2.79it/s]

51.116360452495854
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:24,  2.87it/s]

50.39444379929054
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:01<00:23,  2.89it/s]

52.011898941346075
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:02<00:22,  3.04it/s]

50.9479316354393
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:02<00:22,  2.93it/s]

52.01327658978862
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:02<00:21,  3.05it/s]

51.29950721011723
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:03<00:21,  2.99it/s]

50.94257557289532
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:03<00:21,  2.87it/s]

50.9537679346203
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:03<00:21,  2.93it/s]

52.59643095821044
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:04<00:21,  2.88it/s]

51.79088694145406
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:04<00:20,  2.89it/s]

51.296582296081546
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:04<00:20,  2.95it/s]

50.18030426814833
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:05<00:20,  2.88it/s]

48.711315564525876
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:05<00:19,  2.96it/s]

46.39220620748705
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:05<00:18,  3.01it/s]

48.938263130194514
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:06<00:18,  2.92it/s]

49.985445309421976
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:06<00:18,  2.92it/s]

48.421004515848956
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:06<00:18,  2.89it/s]

46.73448317849832
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:07<00:17,  2.91it/s]

45.04607800460027
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:07<00:18,  2.74it/s]

46.60987998978342
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:07<00:18,  2.73it/s]

45.49543456970162
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:08<00:18,  2.64it/s]

44.35072970576405
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:08<00:17,  2.74it/s]

47.001999189736104
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:09<00:16,  2.83it/s]

49.08302229837311
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:09<00:16,  2.86it/s]

49.12173282550087
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:09<00:15,  2.89it/s]

47.54751729945408
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:10<00:15,  2.88it/s]

43.13056706472473
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:10<00:13,  3.08it/s]

43.0427571020094
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:10<00:13,  3.16it/s]

47.318862029358755
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:10<00:13,  3.12it/s]

49.60877376001068
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:11<00:13,  3.03it/s]

47.850280074050964
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:11<00:13,  2.96it/s]

41.04560859539383
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:12<00:12,  2.94it/s]

43.04701706242342
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:12<00:12,  2.88it/s]

43.921826107788505
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:12<00:12,  2.89it/s]

40.54887316406719
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:13<00:11,  2.94it/s]

39.21892023115761
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:13<00:12,  2.73it/s]

42.44728891510631
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:13<00:11,  2.78it/s]

37.80330749020048
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:14<00:11,  2.68it/s]

37.673814397289014
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:14<00:11,  2.63it/s]

40.224447884405414
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:14<00:10,  2.73it/s]

35.004642083391545
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:15<00:10,  2.68it/s]

33.7425305289381
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:15<00:10,  2.62it/s]

36.27563975884935
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:16<00:10,  2.56it/s]

33.50804133793367
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:16<00:09,  2.64it/s]

35.52939048154697
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:16<00:09,  2.76it/s]

38.2120199018916
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:17<00:08,  2.78it/s]

41.97017613074813
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:17<00:08,  2.82it/s]

37.50150646974099
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:17<00:07,  2.76it/s]

40.41917334823863
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:18<00:07,  2.71it/s]

44.01771994793713
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:18<00:07,  2.62it/s]

47.072557108225666
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:19<00:07,  2.70it/s]

46.95645846437812
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:19<00:06,  2.62it/s]

47.695247759517464
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:19<00:06,  2.59it/s]

45.25348025342529
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:20<00:06,  2.64it/s]

43.65013605722753
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:20<00:05,  2.62it/s]

44.46662322285012
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:20<00:05,  2.61it/s]

44.5847558263051
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:21<00:04,  2.61it/s]

43.13492155585726
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:21<00:04,  2.63it/s]

43.61555497966765
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:22<00:04,  2.60it/s]

44.560515947567254
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:22<00:03,  2.58it/s]

46.03256333766684
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:22<00:03,  2.58it/s]

41.276133876050224
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:23<00:03,  2.57it/s]

40.524435837122624
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:23<00:02,  2.63it/s]

47.11890230927597
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:24<00:02,  2.71it/s]

49.94217762377262
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:24<00:01,  2.61it/s]

48.64617789634856
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:24<00:01,  2.68it/s]

45.16668060188293
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:25<00:01,  2.71it/s]

45.14223629504769
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:25<00:00,  2.71it/s]

48.17437791513955
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:25<00:00,  2.71it/s]

47.28770984771129
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:26<00:00,  2.78it/s]

44.379693774275566
False
False

Warszawa model selection:   0%|                                                                 | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Warszawa model selection:   7%|████▏                                                    | 2/27 [00:00<00:04,  5.15it/s]

1
Order: (0, 0, 0), AICc: 4132.61, ADF p-value: 0.0001, Best_Order: (0, 0, 0), Best_aicc: 4132.61
(0, 0, 1)
2
1
Order: (0, 0, 1), AICc: 3835.36, ADF p-value: 0.0006, Best_Order: (0, 0, 1), Best_aicc: 3835.36
(0, 0, 2)
2

Warszawa model selection:  11%|██████▎                                                  | 3/27 [00:00<00:05,  4.01it/s]

1
Order: (0, 0, 2), AICc: 3746.31, ADF p-value: 0.0005, Best_Order: (0, 0, 2), Best_aicc: 3746.31
(0, 1, 0)
2

Warszawa model selection:  15%|████████▍                                                | 4/27 [00:00<00:05,  4.12it/s]

1
Order: (0, 1, 0), AICc: 3517.48, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3517.48
(0, 1, 1)
2

Warszawa model selection:  19%|██████████▌                                              | 5/27 [00:01<00:05,  3.92it/s]

1
Order: (0, 1, 1), AICc: 3477.49, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3477.49
(0, 1, 2)
2

Warszawa model selection:  22%|████████████▋                                            | 6/27 [00:01<00:06,  3.49it/s]

1
Order: (0, 1, 2), AICc: 3432.18, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(0, 2, 0)
2

Warszawa model selection:  26%|██████████████▊                                          | 7/27 [00:01<00:05,  3.59it/s]

1
Order: (0, 2, 0), AICc: 4066.43, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(0, 2, 1)
2

Warszawa model selection:  30%|████████████████▉                                        | 8/27 [00:02<00:06,  2.92it/s]

1
Order: (0, 2, 1), AICc: 3517.90, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(0, 2, 2)
2

Warszawa model selection:  33%|███████████████████                                      | 9/27 [00:02<00:07,  2.47it/s]

1
Order: (0, 2, 2), AICc: 3476.74, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(1, 0, 0)
2

Warszawa model selection:  37%|████████████████████▋                                   | 10/27 [00:03<00:06,  2.70it/s]

1
Order: (1, 0, 0), AICc: 3524.76, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(1, 0, 1)
2

Warszawa model selection:  41%|██████████████████████▊                                 | 11/27 [00:03<00:06,  2.51it/s]

1
Order: (1, 0, 1), AICc: 3483.21, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(1, 0, 2)
2

Warszawa model selection:  48%|██████████████████████████▉                             | 13/27 [00:04<00:05,  2.74it/s]

1
Order: (1, 0, 2), AICc: 3438.62, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(1, 1, 0)
2
1
Order: (1, 1, 0), AICc: 3498.55, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3432.18
(1, 1, 1)
2

Warszawa model selection:  52%|█████████████████████████████                           | 14/27 [00:04<00:04,  2.76it/s]

1
Order: (1, 1, 1), AICc: 3427.90, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3427.90
(1, 1, 2)
2

Warszawa model selection:  56%|███████████████████████████████                         | 15/27 [00:05<00:04,  2.63it/s]

1
Order: (1, 1, 2), AICc: 3424.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(1, 2, 0)
2
1

Warszawa model selection:  59%|█████████████████████████████████▏                      | 16/27 [00:05<00:03,  3.08it/s]

Order: (1, 2, 0), AICc: 3880.75, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(1, 2, 1)
2

Warszawa model selection:  63%|███████████████████████████████████▎                    | 17/27 [00:05<00:03,  2.60it/s]

1
Order: (1, 2, 1), AICc: 3497.23, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(1, 2, 2)
2

Warszawa model selection:  67%|█████████████████████████████████████▎                  | 18/27 [00:06<00:04,  2.03it/s]

1
Order: (1, 2, 2), AICc: 3471.32, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(2, 0, 0)
2

Warszawa model selection:  70%|███████████████████████████████████████▍                | 19/27 [00:07<00:03,  2.07it/s]

1
Order: (2, 0, 0), AICc: 3499.07, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(2, 0, 1)
2

Warszawa model selection:  74%|█████████████████████████████████████████▍              | 20/27 [00:07<00:03,  2.01it/s]

1
Order: (2, 0, 1), AICc: 3435.06, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(2, 0, 2)
2

Warszawa model selection:  78%|███████████████████████████████████████████▌            | 21/27 [00:08<00:03,  1.88it/s]

1
Order: (2, 0, 2), AICc: 3442.28, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(2, 1, 0)
2

Warszawa model selection:  81%|█████████████████████████████████████████████▋          | 22/27 [00:08<00:02,  2.26it/s]

1
Order: (2, 1, 0), AICc: 3470.00, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(2, 1, 1)
2

Warszawa model selection:  85%|███████████████████████████████████████████████▋        | 23/27 [00:08<00:01,  2.28it/s]

1
Order: (2, 1, 1), AICc: 3429.72, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3424.72
(2, 1, 2)
2

Warszawa model selection:  89%|█████████████████████████████████████████████████▊      | 24/27 [00:09<00:01,  2.13it/s]

1
Order: (2, 1, 2), AICc: 3424.27, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27
(2, 2, 0)
2

Warszawa model selection:  93%|███████████████████████████████████████████████████▊    | 25/27 [00:09<00:00,  2.51it/s]

1
Order: (2, 2, 0), AICc: 3749.22, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27
(2, 2, 1)
2

Warszawa model selection:  96%|█████████████████████████████████████████████████████▉  | 26/27 [00:10<00:00,  2.19it/s]

1
Order: (2, 2, 1), AICc: 3474.27, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27
(2, 2, 2)
2

Warszawa model selection: 100%|████████████████████████████████████████████████████████| 27/27 [00:11<00:00,  2.44it/s]

1
Order: (2, 2, 2), AICc: 3443.71, ADF p-value: 0.0000, Best_Order: (2, 1, 2), Best_aicc: 3424.27

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:35,  2.04it/s]

46.821069225850565
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:01<00:40,  1.76it/s]

50.687773161715086
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:01<00:35,  1.97it/s]

52.184781833692234
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:02<00:36,  1.88it/s]

52.33187437636305
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:02<00:34,  1.95it/s]

50.22031714864876
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:03<00:36,  1.82it/s]

51.47298971337308
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:03<00:38,  1.73it/s]

50.584333247894406
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:04<00:37,  1.72it/s]

51.531237662875014
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:05<00:37,  1.72it/s]

48.35283503525923
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:05<00:34,  1.81it/s]

50.8231913656234
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:06<00:34,  1.80it/s]

52.795969927190626
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:06<00:32,  1.85it/s]

52.60844401705262
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:07<00:33,  1.80it/s]

48.038094835866474
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:07<00:32,  1.82it/s]

47.9149661452117
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:08<00:31,  1.84it/s]

50.35682268059995
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:08<00:30,  1.84it/s]

48.06192443816771
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:09<00:31,  1.80it/s]

47.80198805616523
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:09<00:31,  1.74it/s]

50.377542945109
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:10<00:29,  1.83it/s]

48.1062649930045
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:10<00:27,  1.90it/s]

47.68916216273482
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:11<00:26,  1.98it/s]

46.809490583939876
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:12<00:27,  1.86it/s]

47.282053067007226
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:12<00:26,  1.85it/s]

48.11019670391504
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:13<00:27,  1.80it/s]

45.60974249880543
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:13<00:26,  1.81it/s]

45.11835084880572
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:14<00:26,  1.77it/s]

48.764964464667656
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:14<00:26,  1.75it/s]

48.3480409360308
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:15<00:25,  1.73it/s]

42.35721770348719
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:16<00:24,  1.76it/s]

42.91240877713044
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:16<00:24,  1.73it/s]

39.99562467888545
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:17<00:25,  1.67it/s]

40.79568458935276
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:17<00:25,  1.62it/s]

41.05507556330633
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:18<00:24,  1.62it/s]

38.565262395078065
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:19<00:24,  1.59it/s]

35.85114564862623
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:19<00:22,  1.69it/s]

42.80283695005946
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:20<00:22,  1.65it/s]

43.19136909051876
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:20<00:20,  1.71it/s]

42.028631552756174
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:21<00:21,  1.66it/s]

39.862868130405175
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:22<00:20,  1.65it/s]

40.17253187111105
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:22<00:19,  1.73it/s]

37.73659180541854
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:23<00:19,  1.67it/s]

36.197727954228995
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:23<00:18,  1.66it/s]

37.49641609222998
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:24<00:18,  1.62it/s]

33.89044465582461
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:25<00:17,  1.63it/s]

35.37136292692365
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:25<00:17,  1.60it/s]

37.3994050059425
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:26<00:16,  1.62it/s]

36.690002015108654
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:27<00:16,  1.61it/s]

34.611891339520895
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:27<00:15,  1.61it/s]

36.75770357700703
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:28<00:14,  1.60it/s]

38.31363742662252
1

Analysed test_samples from:  68%|████████████████████████████████████▉                 | 50/73 [00:28<00:14,  1.56it/s]

36.23287649824114
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:29<00:13,  1.57it/s]

36.9766864363922
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:30<00:13,  1.57it/s]

39.770453380762355
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:30<00:12,  1.60it/s]

44.22975597266849
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:31<00:11,  1.64it/s]

43.472436072541356
1

Analysed test_samples from:  75%|████████████████████████████████████████▋             | 55/73 [00:32<00:11,  1.59it/s]

41.88065530699804
1

Analysed test_samples from:  77%|█████████████████████████████████████████▍            | 56/73 [00:32<00:10,  1.57it/s]

41.62159209658565
1

Analysed test_samples from:  78%|██████████████████████████████████████████▏           | 57/73 [00:33<00:10,  1.57it/s]

42.22356388029505
1

Analysed test_samples from:  79%|██████████████████████████████████████████▉           | 58/73 [00:33<00:09,  1.63it/s]

44.28822709207622
1

Analysed test_samples from:  81%|███████████████████████████████████████████▋          | 59/73 [00:34<00:08,  1.64it/s]

46.68435346016454
1

Analysed test_samples from:  82%|████████████████████████████████████████████▍         | 60/73 [00:35<00:07,  1.67it/s]

46.78740780002332
1

Analysed test_samples from:  84%|█████████████████████████████████████████████         | 61/73 [00:35<00:07,  1.70it/s]

46.34622359434884
1

Analysed test_samples from:  85%|█████████████████████████████████████████████▊        | 62/73 [00:36<00:06,  1.71it/s]

46.51023535554077
1

Analysed test_samples from:  86%|██████████████████████████████████████████████▌       | 63/73 [00:36<00:06,  1.63it/s]

45.66363432623487
1

Analysed test_samples from:  88%|███████████████████████████████████████████████▎      | 64/73 [00:37<00:05,  1.59it/s]

41.466159552473286
1

Analysed test_samples from:  89%|████████████████████████████████████████████████      | 65/73 [00:38<00:05,  1.56it/s]

39.83857804991645
1

Analysed test_samples from:  90%|████████████████████████████████████████████████▊     | 66/73 [00:38<00:04,  1.54it/s]

42.55665701951628
1

Analysed test_samples from:  92%|█████████████████████████████████████████████████▌    | 67/73 [00:39<00:03,  1.56it/s]

47.20197101755791
1

Analysed test_samples from:  93%|██████████████████████████████████████████████████▎   | 68/73 [00:40<00:03,  1.55it/s]

47.36750549220996
1

Analysed test_samples from:  95%|███████████████████████████████████████████████████   | 69/73 [00:40<00:02,  1.57it/s]

42.860908068939835
1

Analysed test_samples from:  96%|███████████████████████████████████████████████████▊  | 70/73 [00:41<00:01,  1.53it/s]

43.18316251521779
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:42<00:01,  1.61it/s]

45.718376156288656
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:42<00:00,  1.54it/s]

45.86117832245943
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:43<00:00,  1.68it/s]

44.09246732174286
False
False

Wiedeń model selection:   0%|                                                                   | 0/27 [00:00<?, ?it/s]

(0, 0, 0)
2

Wiedeń model selection:   4%|██▏                                                        | 1/27 [00:00<00:07,  3.52it/s]

1
Order: (0, 0, 0), AICc: 3903.44, ADF p-value: 0.0000, Best_Order: (0, 0, 0), Best_aicc: 3903.44
(0, 0, 1)
2

Wiedeń model selection:   7%|████▎                                                      | 2/27 [00:00<00:07,  3.35it/s]

1
Order: (0, 0, 1), AICc: 3665.69, ADF p-value: 0.0000, Best_Order: (0, 0, 1), Best_aicc: 3665.69
(0, 0, 2)
2

Wiedeń model selection:  11%|██████▌                                                    | 3/27 [00:00<00:06,  3.60it/s]

1
Order: (0, 0, 2), AICc: 3574.13, ADF p-value: 0.0000, Best_Order: (0, 0, 2), Best_aicc: 3574.13
(0, 1, 0)
2

Wiedeń model selection:  15%|████████▋                                                  | 4/27 [00:01<00:05,  3.85it/s]

1
Order: (0, 1, 0), AICc: 3364.85, ADF p-value: 0.0000, Best_Order: (0, 1, 0), Best_aicc: 3364.85
(0, 1, 1)
2
1

Wiedeń model selection:  19%|██████████▉                                                | 5/27 [00:01<00:05,  4.14it/s]

Order: (0, 1, 1), AICc: 3326.69, ADF p-value: 0.0000, Best_Order: (0, 1, 1), Best_aicc: 3326.69
(0, 1, 2)
2

Wiedeń model selection:  22%|█████████████                                              | 6/27 [00:01<00:05,  3.91it/s]

1
Order: (0, 1, 2), AICc: 3281.35, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(0, 2, 0)
2

Wiedeń model selection:  26%|███████████████▎                                           | 7/27 [00:01<00:04,  4.07it/s]

1
Order: (0, 2, 0), AICc: 3879.09, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(0, 2, 1)
2

Wiedeń model selection:  30%|█████████████████▍                                         | 8/27 [00:02<00:05,  3.66it/s]

1
Order: (0, 2, 1), AICc: 3360.08, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(0, 2, 2)
2

Wiedeń model selection:  33%|███████████████████▋                                       | 9/27 [00:02<00:05,  3.44it/s]

1
Order: (0, 2, 2), AICc: 3325.23, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(1, 0, 0)
2

Wiedeń model selection:  37%|█████████████████████▍                                    | 10/27 [00:02<00:04,  3.47it/s]

1
Order: (1, 0, 0), AICc: 3369.36, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(1, 0, 1)
2

Wiedeń model selection:  41%|███████████████████████▋                                  | 11/27 [00:03<00:05,  2.90it/s]

1
Order: (1, 0, 1), AICc: 3332.45, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(1, 0, 2)
2

Wiedeń model selection:  44%|█████████████████████████▊                                | 12/27 [00:03<00:06,  2.33it/s]

1
Order: (1, 0, 2), AICc: 3289.46, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(1, 1, 0)
2

Wiedeń model selection:  48%|███████████████████████████▉                              | 13/27 [00:04<00:05,  2.75it/s]

1
Order: (1, 1, 0), AICc: 3350.85, ADF p-value: 0.0000, Best_Order: (0, 1, 2), Best_aicc: 3281.35
(1, 1, 1)
2

Wiedeń model selection:  52%|██████████████████████████████                            | 14/27 [00:04<00:04,  2.67it/s]

1
Order: (1, 1, 1), AICc: 3278.62, ADF p-value: 0.0000, Best_Order: (1, 1, 1), Best_aicc: 3278.62
(1, 1, 2)
2

Wiedeń model selection:  56%|████████████████████████████████▏                         | 15/27 [00:04<00:04,  2.65it/s]

1
Order: (1, 1, 2), AICc: 3272.46, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(1, 2, 0)
2

Wiedeń model selection:  59%|██████████████████████████████████▎                       | 16/27 [00:05<00:03,  2.93it/s]

1
Order: (1, 2, 0), AICc: 3730.18, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(1, 2, 1)
2

Wiedeń model selection:  63%|████████████████████████████████████▌                     | 17/27 [00:05<00:03,  2.59it/s]

1
Order: (1, 2, 1), AICc: 3347.19, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(1, 2, 2)
2

Wiedeń model selection:  67%|██████████████████████████████████████▋                   | 18/27 [00:06<00:04,  1.93it/s]

1
Order: (1, 2, 2), AICc: 3280.75, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 0, 0)
2

Wiedeń model selection:  70%|████████████████████████████████████████▊                 | 19/27 [00:06<00:03,  2.03it/s]

1
Order: (2, 0, 0), AICc: 3351.08, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 0, 1)
2

Wiedeń model selection:  74%|██████████████████████████████████████████▉               | 20/27 [00:07<00:03,  1.97it/s]

1
Order: (2, 0, 1), AICc: 3285.47, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 0, 2)
2

Wiedeń model selection:  78%|█████████████████████████████████████████████             | 21/27 [00:08<00:03,  1.78it/s]

1
Order: (2, 0, 2), AICc: 3281.86, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 1, 0)
2

Wiedeń model selection:  81%|███████████████████████████████████████████████▎          | 22/27 [00:08<00:02,  2.16it/s]

1
Order: (2, 1, 0), AICc: 3315.59, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 1, 1)
2

Wiedeń model selection:  85%|█████████████████████████████████████████████████▍        | 23/27 [00:08<00:01,  2.34it/s]

1
Order: (2, 1, 1), AICc: 3280.16, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 1, 2)
2

Wiedeń model selection:  89%|███████████████████████████████████████████████████▌      | 24/27 [00:09<00:01,  2.04it/s]

1
Order: (2, 1, 2), AICc: 3274.09, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 2, 0)
2

Wiedeń model selection:  93%|█████████████████████████████████████████████████████▋    | 25/27 [00:09<00:00,  2.26it/s]

1
Order: (2, 2, 0), AICc: 3620.57, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 2, 1)
2

Wiedeń model selection:  96%|███████████████████████████████████████████████████████▊  | 26/27 [00:10<00:00,  2.26it/s]

1
Order: (2, 2, 1), AICc: 3320.09, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46
(2, 2, 2)
2

Wiedeń model selection: 100%|██████████████████████████████████████████████████████████| 27/27 [00:10<00:00,  2.47it/s]

1
Order: (2, 2, 2), AICc: 3289.88, ADF p-value: 0.0000, Best_Order: (1, 1, 2), Best_aicc: 3272.46

Analysed test_samples from:   0%|                                                               | 0/73 [00:00<?, ?it/s]

1

Analysed test_samples from:   1%|▊                                                      | 1/73 [00:00<00:25,  2.86it/s]

57.8449434787823
1

Analysed test_samples from:   3%|█▌                                                     | 2/73 [00:00<00:27,  2.59it/s]

62.683339461989114
1

Analysed test_samples from:   4%|██▎                                                    | 3/73 [00:01<00:31,  2.19it/s]

59.37077225819115
1

Analysed test_samples from:   5%|███                                                    | 4/73 [00:01<00:30,  2.27it/s]

57.74631227123143
1

Analysed test_samples from:   7%|███▊                                                   | 5/73 [00:02<00:28,  2.42it/s]

56.33440335380239
1

Analysed test_samples from:   8%|████▌                                                  | 6/73 [00:02<00:26,  2.50it/s]

59.857299010301865
1

Analysed test_samples from:  10%|█████▎                                                 | 7/73 [00:02<00:27,  2.44it/s]

59.6887483008113
1

Analysed test_samples from:  11%|██████                                                 | 8/73 [00:03<00:27,  2.40it/s]

57.03954861785948
1

Analysed test_samples from:  12%|██████▊                                                | 9/73 [00:03<00:27,  2.34it/s]

57.13819755294368
1

Analysed test_samples from:  14%|███████▍                                              | 10/73 [00:04<00:26,  2.38it/s]

56.05899567526647
1

Analysed test_samples from:  15%|████████▏                                             | 11/73 [00:04<00:26,  2.33it/s]

55.76440588514446
1

Analysed test_samples from:  16%|████████▉                                             | 12/73 [00:05<00:26,  2.30it/s]

56.43013013328056
1

Analysed test_samples from:  18%|█████████▌                                            | 13/73 [00:05<00:26,  2.25it/s]

57.124519123091474
1

Analysed test_samples from:  19%|██████████▎                                           | 14/73 [00:05<00:25,  2.31it/s]

57.14801958011789
1

Analysed test_samples from:  21%|███████████                                           | 15/73 [00:06<00:24,  2.35it/s]

51.9351261623211
1

Analysed test_samples from:  22%|███████████▊                                          | 16/73 [00:06<00:23,  2.46it/s]

54.57032860227861
1

Analysed test_samples from:  23%|████████████▌                                         | 17/73 [00:07<00:22,  2.49it/s]

55.3991129310446
1

Analysed test_samples from:  25%|█████████████▎                                        | 18/73 [00:07<00:22,  2.43it/s]

55.88986234021294
1

Analysed test_samples from:  26%|██████████████                                        | 19/73 [00:07<00:22,  2.42it/s]

55.74093893652491
1

Analysed test_samples from:  27%|██████████████▊                                       | 20/73 [00:08<00:21,  2.42it/s]

53.059578050800454
1

Analysed test_samples from:  29%|███████████████▌                                      | 21/73 [00:08<00:21,  2.47it/s]

50.26464816993388
1

Analysed test_samples from:  30%|████████████████▎                                     | 22/73 [00:09<00:20,  2.52it/s]

51.2548956655222
1

Analysed test_samples from:  32%|█████████████████                                     | 23/73 [00:09<00:19,  2.53it/s]

52.054519492912995
1

Analysed test_samples from:  33%|█████████████████▊                                    | 24/73 [00:09<00:19,  2.48it/s]

50.928670588228
1

Analysed test_samples from:  34%|██████████████████▍                                   | 25/73 [00:10<00:19,  2.42it/s]

49.84766431215074
1

Analysed test_samples from:  36%|███████████████████▏                                  | 26/73 [00:10<00:18,  2.48it/s]

56.75859612369776
1

Analysed test_samples from:  37%|███████████████████▉                                  | 27/73 [00:11<00:18,  2.43it/s]

55.264319793396794
1

Analysed test_samples from:  38%|████████████████████▋                                 | 28/73 [00:11<00:18,  2.43it/s]

54.210523093821294
1

Analysed test_samples from:  40%|█████████████████████▍                                | 29/73 [00:12<00:18,  2.44it/s]

49.359766857082334
1

Analysed test_samples from:  41%|██████████████████████▏                               | 30/73 [00:12<00:17,  2.42it/s]

48.85940792257731
1

Analysed test_samples from:  42%|██████████████████████▉                               | 31/73 [00:12<00:17,  2.42it/s]

46.72366396670721
1

Analysed test_samples from:  44%|███████████████████████▋                              | 32/73 [00:13<00:17,  2.40it/s]

54.393783452209746
1

Analysed test_samples from:  45%|████████████████████████▍                             | 33/73 [00:13<00:16,  2.42it/s]

52.740114124020806
1

Analysed test_samples from:  47%|█████████████████████████▏                            | 34/73 [00:14<00:16,  2.42it/s]

45.756286240857804
1

Analysed test_samples from:  48%|█████████████████████████▉                            | 35/73 [00:14<00:15,  2.44it/s]

46.909222072419226
1

Analysed test_samples from:  49%|██████████████████████████▋                           | 36/73 [00:14<00:15,  2.45it/s]

49.347706712951805
1

Analysed test_samples from:  51%|███████████████████████████▎                          | 37/73 [00:15<00:14,  2.44it/s]

44.11684142456111
1

Analysed test_samples from:  52%|████████████████████████████                          | 38/73 [00:15<00:14,  2.48it/s]

44.49814772830541
1

Analysed test_samples from:  53%|████████████████████████████▊                         | 39/73 [00:16<00:13,  2.50it/s]

48.024888826862636
1

Analysed test_samples from:  55%|█████████████████████████████▌                        | 40/73 [00:16<00:13,  2.41it/s]

44.924229242657
1

Analysed test_samples from:  56%|██████████████████████████████▎                       | 41/73 [00:16<00:13,  2.46it/s]

41.50723892255159
1

Analysed test_samples from:  58%|███████████████████████████████                       | 42/73 [00:17<00:13,  2.35it/s]

41.64491860788039
1

Analysed test_samples from:  59%|███████████████████████████████▊                      | 43/73 [00:17<00:12,  2.38it/s]

41.722504603576965
1

Analysed test_samples from:  60%|████████████████████████████████▌                     | 44/73 [00:18<00:12,  2.31it/s]

40.34241618016443
1

Analysed test_samples from:  62%|█████████████████████████████████▎                    | 45/73 [00:18<00:12,  2.32it/s]

40.108723593561294
1

Analysed test_samples from:  63%|██████████████████████████████████                    | 46/73 [00:19<00:12,  2.22it/s]

36.81154899469463
1

Analysed test_samples from:  64%|██████████████████████████████████▊                   | 47/73 [00:19<00:11,  2.30it/s]

39.65189657134642
1

Analysed test_samples from:  66%|███████████████████████████████████▌                  | 48/73 [00:20<00:11,  2.25it/s]

41.514490074811846
1

Analysed test_samples from:  67%|████████████████████████████████████▏                 | 49/73 [00:20<00:10,  2.26it/s]

43.89157527070361
1

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40.91405640738739
1

Analysed test_samples from:  70%|█████████████████████████████████████▋                | 51/73 [00:21<00:09,  2.38it/s]

41.317910338500816
1

Analysed test_samples from:  71%|██████████████████████████████████████▍               | 52/73 [00:21<00:08,  2.43it/s]

45.459669397172355
1

Analysed test_samples from:  73%|███████████████████████████████████████▏              | 53/73 [00:22<00:08,  2.34it/s]

45.323642911167845
1

Analysed test_samples from:  74%|███████████████████████████████████████▉              | 54/73 [00:22<00:08,  2.27it/s]

51.51232115170022
1

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47.18462437502026
1

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50.60343242112565
1

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45.888393916654735
1

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47.92214152198338
1

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47.751770770057306
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1

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43.98838789761642
1

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48.91626304222482
1

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51.58605600394259
1

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47.250393324562516
1

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44.608468489510656
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53.646549233053356
1

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54.23209794326293
1

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52.1021871201871
1

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51.92919123252516
1

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45.45830805967085
1

Analysed test_samples from:  97%|████████████████████████████████████████████████████▌ | 71/73 [00:29<00:00,  2.36it/s]

51.636680625342834
1

Analysed test_samples from:  99%|█████████████████████████████████████████████████████▎| 72/73 [00:30<00:00,  2.41it/s]

53.11865939860117
1

Analysed test_samples from: 100%|██████████████████████████████████████████████████████| 73/73 [00:30<00:00,  2.40it/s]

44.4414412699819
False
False

In [11]:

from pmdarima import auto_arima

from pmdarima import auto_arima

In [12]:

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm

def create_lag_features(df, lags, target_col='TEMP'):
    for lag in lags:
        df[f'{target_col}_lag{lag}'] = df[target_col].shift(lag)
    return df

# Config
lags = [1, 2, 3,7]  # example lags
exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB']
all_features = [f'TEMP_lag{l}' for l in lags] + exog_vars

forecast_results = []

for city, df in city_dataframes.items():
    data = df.copy()
    data.reset_index(inplace=True)
    data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce')
    data['Month'] = data['DATE'].dt.month
    data.set_index('DATE', inplace=True)
    
    # Filter last 2 years
    cutoff_date = data.index.max() - pd.DateOffset(years=2)
    data = data[data.index >= cutoff_date]
    
    data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce')
    data = create_lag_features(data, lags)
    data.dropna(inplace=True)

    # Split
    n_total = len(data)
    n_test = int(0.1 * n_total)
    n_val = int(0.15 * (n_total - n_test))
    n_train = n_total - n_val - n_test

    train = data.iloc[:n_train]
    val = data.iloc[n_train:n_train + n_val]
    test = data.iloc[n_train + n_val:]

    combined = pd.concat([train, val])
    # predictions, actuals = [], []
    predictions, lowers, uppers, actuals = [], [], [], []

    for t in tqdm(range(len(test)), desc=f"Rolling RF for {city}"):
        try:
            # Define X and y for model training
            X_train = combined[all_features]
            y_train = combined['TEMP']
            
            # Fit model
            model = RandomForestRegressor(n_estimators=100, random_state=42)
            model.fit(X_train, y_train)

            # Prepare test sample
            x_next = test[all_features].iloc[[t]]
            y_next = test['TEMP'].iloc[t]
            # y_pred = model.predict(x_next)[0]
            all_preds = np.array([tree.predict(x_next)[0] for tree in model.estimators_])
            pred_mean = np.mean(all_preds)
            ci_lower = np.percentile(all_preds, 2.5)
            ci_upper = np.percentile(all_preds, 97.5)

            
            # predictions.append(y_pred)
            predictions.append(pred_mean)
            lowers.append(ci_lower)
            uppers.append(ci_upper)
            actuals.append(y_next)

            # Add row to combined history (simulate rolling)
            next_row = test.iloc[[t]].copy()
            next_row['TEMP'] = pred_mean  # use predicted value
            combined = pd.concat([combined, next_row])
        except Exception as e:
            print(f"{city} step {t} failed: {e}")
            predictions.append(np.nan)
            actuals.append(test['TEMP'].iloc[t])

    # Evaluation
    y_true = np.array(actuals)
    y_pred = np.array(predictions)
    mask = ~np.isnan(y_pred)

    if mask.sum() == 0:
        print(f"{city} — no valid predictions")
        continue

    mae = mean_absolute_error(y_true[mask], y_pred[mask])
    rmse = np.sqrt(mean_squared_error(y_true[mask], y_pred[mask]))

    forecast_results.append({
        'City': city,
        'Model': 'RandomForest',
        'Test MAE': mae,
        'Test RMSE': rmse
    })

    # Plot
    test_index = test.index[:len(y_pred)]
    plt.figure(figsize=(15, 6))
    plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue')
    plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange')
    plt.plot(test_index, y_true, label='Test Actual', color='green')
    plt.fill_between(test_index, lowers, uppers, color='red', alpha=0.3, label='95% CI')
    plt.plot(test_index, y_pred, label='Forecast (RF)', color='red')
    plt.title(f"Random Forest Rolling Forecast — {city}")
    plt.xlabel("Date")
    plt.ylabel("Temperature")
    plt.legend()
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(f"../../plots/{city}_randomforest.png")
    plt.close()

from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error, mean_squared_error import numpy as np import pandas as pd import matplotlib.pyplot as plt from tqdm import tqdm def create_lag_features(df, lags, target_col='TEMP'): for lag in lags: df[f'{target_col}_lag{lag}'] = df[target_col].shift(lag) return df # Config lags = [1, 2, 3,7] # example lags exog_vars = ['DEWP', 'Month', 'PRCP', 'VISIB'] all_features = [f'TEMP_lag{l}' for l in lags] + exog_vars forecast_results = [] for city, df in city_dataframes.items(): data = df.copy() data.reset_index(inplace=True) data['DATE'] = pd.to_datetime(data['DATE'], errors='coerce') data['Month'] = data['DATE'].dt.month data.set_index('DATE', inplace=True) # Filter last 2 years cutoff_date = data.index.max() - pd.DateOffset(years=2) data = data[data.index >= cutoff_date] data[exog_vars + ['TEMP']] = data[exog_vars + ['TEMP']].apply(pd.to_numeric, errors='coerce') data = create_lag_features(data, lags) data.dropna(inplace=True) # Split n_total = len(data) n_test = int(0.1 * n_total) n_val = int(0.15 * (n_total - n_test)) n_train = n_total - n_val - n_test train = data.iloc[:n_train] val = data.iloc[n_train:n_train + n_val] test = data.iloc[n_train + n_val:] combined = pd.concat([train, val]) # predictions, actuals = [], [] predictions, lowers, uppers, actuals = [], [], [], [] for t in tqdm(range(len(test)), desc=f"Rolling RF for {city}"): try: # Define X and y for model training X_train = combined[all_features] y_train = combined['TEMP'] # Fit model model = RandomForestRegressor(n_estimators=100, random_state=42) model.fit(X_train, y_train) # Prepare test sample x_next = test[all_features].iloc[[t]] y_next = test['TEMP'].iloc[t] # y_pred = model.predict(x_next)[0] all_preds = np.array([tree.predict(x_next)[0] for tree in model.estimators_]) pred_mean = np.mean(all_preds) ci_lower = np.percentile(all_preds, 2.5) ci_upper = np.percentile(all_preds, 97.5) # predictions.append(y_pred) predictions.append(pred_mean) lowers.append(ci_lower) uppers.append(ci_upper) actuals.append(y_next) # Add row to combined history (simulate rolling) next_row = test.iloc[[t]].copy() next_row['TEMP'] = pred_mean # use predicted value combined = pd.concat([combined, next_row]) except Exception as e: print(f"{city} step {t} failed: {e}") predictions.append(np.nan) actuals.append(test['TEMP'].iloc[t]) # Evaluation y_true = np.array(actuals) y_pred = np.array(predictions) mask = ~np.isnan(y_pred) if mask.sum() == 0: print(f"{city} — no valid predictions") continue mae = mean_absolute_error(y_true[mask], y_pred[mask]) rmse = np.sqrt(mean_squared_error(y_true[mask], y_pred[mask])) forecast_results.append({ 'City': city, 'Model': 'RandomForest', 'Test MAE': mae, 'Test RMSE': rmse }) # Plot test_index = test.index[:len(y_pred)] plt.figure(figsize=(15, 6)) plt.plot(data.index[:n_train], data['TEMP'].iloc[:n_train], label='Train', color='blue') plt.plot(data.index[n_train:n_train+n_val], data['TEMP'].iloc[n_train:n_train+n_val], label='Val', color='orange') plt.plot(test_index, y_true, label='Test Actual', color='green') plt.fill_between(test_index, lowers, uppers, color='red', alpha=0.3, label='95% CI') plt.plot(test_index, y_pred, label='Forecast (RF)', color='red') plt.title(f"Random Forest Rolling Forecast — {city}") plt.xlabel("Date") plt.ylabel("Temperature") plt.legend() plt.grid(True) plt.tight_layout() plt.savefig(f"../../plots/{city}_randomforest.png") plt.close()

Rolling RF for Ateny: 100%|████████████████████████████████████████████████████████████| 72/72 [00:23<00:00,  3.01it/s]
Rolling RF for Berlin: 100%|███████████████████████████████████████████████████████████| 72/72 [00:24<00:00,  2.90it/s]
Rolling RF for Bruksela: 100%|█████████████████████████████████████████████████████████| 72/72 [00:25<00:00,  2.79it/s]
Rolling RF for Budapeszt: 100%|████████████████████████████████████████████████████████| 72/72 [00:24<00:00,  2.96it/s]
Rolling RF for Lisbona: 100%|██████████████████████████████████████████████████████████| 69/69 [00:22<00:00,  3.09it/s]
Rolling RF for Londyn: 100%|███████████████████████████████████████████████████████████| 71/71 [00:28<00:00,  2.52it/s]
Rolling RF for Madryd: 100%|███████████████████████████████████████████████████████████| 72/72 [00:25<00:00,  2.86it/s]
Rolling RF for Moskwa: 100%|███████████████████████████████████████████████████████████| 28/28 [00:06<00:00,  4.61it/s]
Rolling RF for Paryż: 100%|████████████████████████████████████████████████████████████| 72/72 [00:25<00:00,  2.79it/s]
Rolling RF for Praga: 100%|████████████████████████████████████████████████████████████| 72/72 [00:26<00:00,  2.77it/s]
Rolling RF for Warszawa: 100%|█████████████████████████████████████████████████████████| 72/72 [00:25<00:00,  2.80it/s]
Rolling RF for Wiedeń: 100%|███████████████████████████████████████████████████████████| 72/72 [00:26<00:00,  2.76it/s]

In [13]:

forecast_results

forecast_results

Out[13]:

[{'City': 'Ateny',
  'Model': 'RandomForest',
  'Test MAE': 2.5913532538431117,
  'Test RMSE': 3.3110576437363473},
 {'City': 'Berlin',
  'Model': 'RandomForest',
  'Test MAE': 2.213854448364975,
  'Test RMSE': 2.763150691958107},
 {'City': 'Bruksela',
  'Model': 'RandomForest',
  'Test MAE': 2.104675499193953,
  'Test RMSE': 2.5358631943032495},
 {'City': 'Budapeszt',
  'Model': 'RandomForest',
  'Test MAE': 2.975801361490591,
  'Test RMSE': 3.5485013812483865},
 {'City': 'Lisbona',
  'Model': 'RandomForest',
  'Test MAE': 1.8826458219314957,
  'Test RMSE': 2.5379073770421536},
 {'City': 'Londyn',
  'Model': 'RandomForest',
  'Test MAE': 1.613961442661724,
  'Test RMSE': 1.9668790687150095},
 {'City': 'Madryd',
  'Model': 'RandomForest',
  'Test MAE': 2.404226537265454,
  'Test RMSE': 2.965278670613778},
 {'City': 'Moskwa',
  'Model': 'RandomForest',
  'Test MAE': 3.6079551258404217,
  'Test RMSE': 4.908083692679886},
 {'City': 'Paryż',
  'Model': 'RandomForest',
  'Test MAE': 2.098009928791851,
  'Test RMSE': 2.5611668885396877},
 {'City': 'Praga',
  'Model': 'RandomForest',
  'Test MAE': 2.0448051753992456,
  'Test RMSE': 2.687780845547912},
 {'City': 'Warszawa',
  'Model': 'RandomForest',
  'Test MAE': 2.1570712354846355,
  'Test RMSE': 2.584586108583517},
 {'City': 'Wiedeń',
  'Model': 'RandomForest',
  'Test MAE': 2.391423579789416,
  'Test RMSE': 3.075091837920412}]